Repository: facebookresearch/dinov2 Branch: main Commit: 7b187bd4df8e Files: 201 Total size: 2.1 MB Directory structure: gitextract_rhfo6abb/ ├── .github/ │ └── workflows/ │ └── lint.yaml ├── .gitignore ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── LICENSE_CELL_DINO_CODE ├── LICENSE_CELL_DINO_MODELS ├── LICENSE_XRAY_DINO_MODEL ├── MODEL_CARD.md ├── README.md ├── conda-extras.yaml ├── conda.yaml ├── dinov2/ │ ├── __init__.py │ ├── configs/ │ │ ├── __init__.py │ │ ├── eval/ │ │ │ ├── cell_dino/ │ │ │ │ ├── vitl16_channel_adaptive_pretrain.yaml │ │ │ │ └── vitl16_pretrain.yaml │ │ │ ├── vitb14_pretrain.yaml │ │ │ ├── vitb14_reg4_pretrain.yaml │ │ │ ├── vitg14_pretrain.yaml │ │ │ ├── vitg14_reg4_pretrain.yaml │ │ │ ├── vitl14_pretrain.yaml │ │ │ ├── vitl14_reg4_pretrain.yaml │ │ │ ├── vits14_pretrain.yaml │ │ │ └── vits14_reg4_pretrain.yaml │ │ ├── ssl_default_config.yaml │ │ └── train/ │ │ ├── cell_dino/ │ │ │ ├── vitl16_boc_hpafov.yaml │ │ │ ├── vitl16_hpafov.yaml │ │ │ └── vitl16_hpaone.yaml │ │ ├── vitg14.yaml │ │ ├── vitl14.yaml │ │ └── vitl16_short.yaml │ ├── data/ │ │ ├── __init__.py │ │ ├── accumulators.py │ │ ├── adapters.py │ │ ├── augmentations.py │ │ ├── cell_dino/ │ │ │ ├── augmentations.py │ │ │ └── transforms.py │ │ ├── collate.py │ │ ├── datasets/ │ │ │ ├── __init__.py │ │ │ ├── cell_dino/ │ │ │ │ ├── chammi_cp.py │ │ │ │ ├── chammi_hpa.py │ │ │ │ ├── chammi_wtc.py │ │ │ │ ├── hpafov.py │ │ │ │ └── hpaone.py │ │ │ ├── decoders.py │ │ │ ├── extended.py │ │ │ ├── image_net.py │ │ │ └── image_net_22k.py │ │ ├── loaders.py │ │ ├── masking.py │ │ ├── samplers.py │ │ └── transforms.py │ ├── distributed/ │ │ └── __init__.py │ ├── eval/ │ │ ├── __init__.py │ │ ├── cell_dino/ │ │ │ ├── knn.py │ │ │ ├── linear.py │ │ │ └── utils.py │ │ ├── depth/ │ │ │ ├── __init__.py │ │ │ ├── models/ │ │ │ │ ├── __init__.py │ │ │ │ ├── backbones/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── vision_transformer.py │ │ │ │ ├── builder.py │ │ │ │ ├── decode_heads/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── decode_head.py │ │ │ │ │ ├── dpt_head.py │ │ │ │ │ └── linear_head.py │ │ │ │ ├── depther/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── base.py │ │ │ │ │ └── encoder_decoder.py │ │ │ │ └── losses/ │ │ │ │ ├── __init__.py │ │ │ │ ├── gradientloss.py │ │ │ │ └── sigloss.py │ │ │ └── ops/ │ │ │ ├── __init__.py │ │ │ └── wrappers.py │ │ ├── knn.py │ │ ├── linear.py │ │ ├── log_regression.py │ │ ├── metrics.py │ │ ├── segmentation/ │ │ │ ├── __init__.py │ │ │ ├── hooks/ │ │ │ │ ├── __init__.py │ │ │ │ └── optimizer.py │ │ │ ├── models/ │ │ │ │ ├── __init__.py │ │ │ │ ├── backbones/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── vision_transformer.py │ │ │ │ └── decode_heads/ │ │ │ │ ├── __init__.py │ │ │ │ └── linear_head.py │ │ │ └── utils/ │ │ │ ├── __init__.py │ │ │ └── colormaps.py │ │ ├── segmentation_m2f/ │ │ │ ├── __init__.py │ │ │ ├── core/ │ │ │ │ ├── __init__.py │ │ │ │ ├── anchor/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── builder.py │ │ │ │ │ └── point_generator.py │ │ │ │ ├── box/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── builder.py │ │ │ │ │ └── samplers/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── base_sampler.py │ │ │ │ │ ├── mask_pseudo_sampler.py │ │ │ │ │ ├── mask_sampling_result.py │ │ │ │ │ └── sampling_result.py │ │ │ │ └── utils/ │ │ │ │ ├── __init__.py │ │ │ │ ├── dist_utils.py │ │ │ │ └── misc.py │ │ │ ├── models/ │ │ │ │ ├── __init__.py │ │ │ │ ├── backbones/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── adapter_modules.py │ │ │ │ │ ├── drop_path.py │ │ │ │ │ ├── vit.py │ │ │ │ │ └── vit_adapter.py │ │ │ │ ├── builder.py │ │ │ │ ├── decode_heads/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── mask2former_head.py │ │ │ │ ├── losses/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── cross_entropy_loss.py │ │ │ │ │ ├── dice_loss.py │ │ │ │ │ └── match_costs.py │ │ │ │ ├── plugins/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── msdeformattn_pixel_decoder.py │ │ │ │ ├── segmentors/ │ │ │ │ │ ├── __init__.py │ │ │ │ │ └── encoder_decoder_mask2former.py │ │ │ │ └── utils/ │ │ │ │ ├── __init__.py │ │ │ │ ├── assigner.py │ │ │ │ ├── point_sample.py │ │ │ │ ├── positional_encoding.py │ │ │ │ └── transformer.py │ │ │ └── ops/ │ │ │ └── modules/ │ │ │ ├── __init__.py │ │ │ └── ms_deform_attn.py │ │ ├── setup.py │ │ └── utils.py │ ├── fsdp/ │ │ └── __init__.py │ ├── hub/ │ │ ├── __init__.py │ │ ├── backbones.py │ │ ├── cell_dino/ │ │ │ └── backbones.py │ │ ├── classifiers.py │ │ ├── depth/ │ │ │ ├── __init__.py │ │ │ ├── decode_heads.py │ │ │ ├── encoder_decoder.py │ │ │ └── ops.py │ │ ├── depthers.py │ │ ├── dinotxt.py │ │ ├── text/ │ │ │ ├── dinotxt_model.py │ │ │ ├── dinov2_wrapper.py │ │ │ ├── text_tower.py │ │ │ ├── text_transformer.py │ │ │ ├── tokenizer.py │ │ │ └── vision_tower.py │ │ ├── utils.py │ │ └── xray_dino/ │ │ └── backbones.py │ ├── layers/ │ │ ├── __init__.py │ │ ├── attention.py │ │ ├── block.py │ │ ├── dino_head.py │ │ ├── drop_path.py │ │ ├── layer_scale.py │ │ ├── mlp.py │ │ ├── patch_embed.py │ │ └── swiglu_ffn.py │ ├── logging/ │ │ ├── __init__.py │ │ └── helpers.py │ ├── loss/ │ │ ├── __init__.py │ │ ├── dino_clstoken_loss.py │ │ ├── ibot_patch_loss.py │ │ └── koleo_loss.py │ ├── models/ │ │ ├── __init__.py │ │ └── vision_transformer.py │ ├── run/ │ │ ├── __init__.py │ │ ├── eval/ │ │ │ ├── cell_dino/ │ │ │ │ ├── knn.py │ │ │ │ └── linear.py │ │ │ ├── knn.py │ │ │ ├── linear.py │ │ │ └── log_regression.py │ │ ├── submit.py │ │ └── train/ │ │ └── train.py │ ├── thirdparty/ │ │ └── CLIP/ │ │ ├── LICENSE │ │ └── clip/ │ │ └── simple_tokenizer.py │ ├── train/ │ │ ├── __init__.py │ │ ├── ssl_meta_arch.py │ │ └── train.py │ └── utils/ │ ├── __init__.py │ ├── checkpoint.py │ ├── cluster.py │ ├── config.py │ ├── dtype.py │ ├── param_groups.py │ └── utils.py ├── docs/ │ ├── README_CELL_DINO.md │ └── README_CHANNEL_ADAPTIVE_DINO.md ├── hubconf.py ├── notebooks/ │ ├── cell_dino/ │ │ └── inference.ipynb │ ├── depth_estimation.ipynb │ ├── dinotxt.ipynb │ └── semantic_segmentation.ipynb ├── pyproject.toml ├── requirements-dev.txt ├── requirements-extras.txt ├── requirements.txt ├── scripts/ │ ├── cell_dino/ │ │ ├── launcher_knn_eval_on_chammi.sh │ │ └── launcher_linear_eval_on_chammi.sh │ └── lint.sh ├── setup.cfg └── setup.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/lint.yaml ================================================ name: Lint on: push: branches: - main pull_request: branches: - main jobs: run-linters: name: Run linters runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v3 - name: Set up Python uses: actions/setup-python@v4 with: python-version: 3.9 cache: 'pip' cache-dependency-path: '**/requirements*.txt' - name: Install Python (development) dependencies run: | pip install -r requirements-dev.txt - name: Run flake8 run: | flake8 - name: Run black if: always() run: | black --check dinov2 - name: Run pylint if: always() run: | pylint --exit-zero dinov2 ================================================ FILE: .gitignore ================================================ build/ dist/ *.egg-info/ **/__pycache__/ **/.ipynb_checkpoints **/.ipynb_checkpoints/** *.swp .vscode/ ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive 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Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies within all project spaces, and it also applies when an individual is representing the project or its community in public spaces. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. This Code of Conduct also applies outside the project spaces when there is a reasonable belief that an individual's behavior may have a negative impact on the project or its community. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at . All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing to DINOv2 We want to make contributing to this project as easy and transparent as possible. ## Pull Requests We actively welcome your pull requests. 1. Fork the repo and create your branch from `main`. 2. If you've added code that should be tested, add tests. 3. If you've changed APIs, update the documentation. 4. 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Except as provided in this Agreement, no other modification or addition to any provision of this Agreement will be binding unless it is in writing and signed by an authorized representative of both you and Meta. ================================================ FILE: MODEL_CARD.md ================================================ # Model Card for DINOv2-S/B/L/g These are Vision Transformer models trained following the method described in the papers: "DINOv2: Learning Robust Visual Features without Supervision" and "Vision Transformers Need Registers". We provide 8 models: - 1 ViT-g trained from scratch with 3 ViT-S/B/L models distilled from the ViT-g, without registers. - 1 ViT-g trained from scratch with 3 ViT-S/B/L models distilled from the ViT-g, with registers. ## Model Details The model takes an image as input and returns a class token and patch tokens, and optionally 4 register tokens. The embedding dimension is: - 384 for ViT-S. - 768 for ViT-B. - 1024 for ViT-L. - 1536 for ViT-g. The models follow a Transformer architecture, with a patch size of 14. In the case of registers, we add 4 register tokens, learned during training, to the input sequence after the patch embedding. For a 224x224 image, this results in 1 class token + 256 patch tokens, and optionally 4 register tokens. The models can accept larger images provided the image shapes are multiples of the patch size (14). If this condition is not verified, the model will crop to the closest smaller multiple of the patch size. ### Model Description - **Developed by:** Meta AI - **Model type:** Vision Transformer - **License:** Apache License 2.0 - **Repository:** https://github.com/facebookresearch/dinov2 - **Paper:** https://arxiv.org/abs/2304.07193 - **Demo:** https://dinov2.metademolab.com/ ## Uses The models are vision backbones providing multi-purpose features for downstream tasks. ### Direct Use The models can be used without fine-tuning, with downstream classifiers as simple as linear layers, to obtain competitive results: - on depth estimation, semantic segmentation, using linear layers. - on image classification, using k-NN classifiers on the class token. - on image classification, with logistic regression classifiers applied on the class token. - on image classification, with a linear layer applied on the class token and the average of the patch tokens. - on image retrieval using nearest neighbors. ### Downstream Use It is technically possible to perform fine-tuning on the models, for small gains (we measured +2% on ImageNet-1k classification). We recommend keeping this as a very last step and only when necessary, as the features already provide good performance out-of-the-box. ## Bias, Risks, and Limitations Despite improvements thanks to the training method not using annotations, we still observe significant biases in our models toward rich households from Western countries. ### Recommendations We expect fine-tuning will increase the biases in the features produced by the model as they will be tuned to the fine-tuning labels. ## How to Get Started with the Model Use the code below to get started with the model. ```python import torch # DINOv2 dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14') dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14') dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14') dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14') # DINOv2 with registers dinov2_vits14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14_reg') dinov2_vitb14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14_reg') dinov2_vitl14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14_reg') dinov2_vitg14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14_reg') ``` ## Training Details ### Training Data - **Training data:** LVD-142M (see paper) - **Training regime:** fp16 using PyTorch-FSDP mixed-precision. ### Training Procedure - **Training objective:** - DINO self-distillation loss with multi-crop - iBOT masked-image modeling loss - KoLeo regularization on [CLS] tokens - **Architectures:** - ViT-S (21M params): Patch size 14, embedding dimension 384, 6 heads, MLP FFN - ViT-B (86M params): Patch size 14, embedding dimension 768, 12 heads, MLP FFN - ViT-L (0.3B params): Patch size 14, embedding dimension 1024, 16 heads, MLP FFN - ViT-g (1.1B params): Patch size 14, embedding dimension 1536, 24 heads, SwiGLU FFN - **Distillation:** - Distillation follows the standard DINOv2 pretraining procedure, except the teacher is a pretrained ViT-g, frozen. ## Evaluation We refer users to the associated papers for the evaluation protocols.
ImageNet-1k NYU-Depth v2 SUN-RGBD ADE20k iNaturalist 2018 Oxford-H
model with
registers		 classif. (acc) classif. (acc) classif. V2 (acc) depth (RMSE) depth (RMSE) segm. (mAP) classif. (acc) retrieval (mAP)
k-NN linear linear linear
4 layers		 NYU-D transfer multiscale linear nearest neighbor
ViT-S/14 :x: 79.0% 81.1% 70.8% 0.417 0.431 47.2 69.5% 43.2
ViT-S/14 :white_check_mark: 79.1% 80.9% 71.0% N/A N/A N/A 67.6% 39.5
ViT-B/14 :x: 82.1% 84.5% 74.9% 0.362 0.400 51.3 76.3% 49.5
ViT-B/14 :white_check_mark: 82.0% 84.6% 75.6% N/A N/A N/A 73.8% 51.0
ViT-L/14 :x: 83.5% 86.3% 77.6% 0.333 0.396 53.1 79.8% 54.0
ViT-L/14 :white_check_mark: 83.8% 86.7% 78.5% N/A N/A N/A 80.9% 55.7
ViT-g/14 :x: 83.5% 86.5% 78.4% 0.298 0.362 53.0 81.6% 52.3
ViT-g/14 :white_check_mark: 83.7% 87.1% 78.8% N/A N/A N/A 81.5% 58.2
## Environmental Impact - **Hardware Type:** Nvidia A100 - **Hours used:** 22,000 for ViT-g, 4,500 for ViT-S distillation, 5,300 for ViT-B distillation, 8,000 for ViT-L distillation - **Cloud Provider:** Private infra - **Compute Region:** USA - **Carbon Emitted:** 7t CO2eq #### Hardware Nvidia A100 GPUs #### Software PyTorch 2.0, xFormers 0.0.18 **BibTeX** ``` @misc{oquab2023dinov2, title={DINOv2: Learning Robust Visual Features without Supervision}, author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr}, journal={arXiv:2304.07193}, year={2023} } @misc{darcet2023vitneedreg, title={Vision Transformers Need Registers}, author={Darcet, Timothée and Oquab, Maxime and Mairal, Julien and Bojanowski, Piotr}, journal={arXiv:2309.16588}, year={2023} } ``` ================================================ FILE: README.md ================================================ :new: [2025-12-18] *Added support for loading XRay-DINO backbone following [Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning](https://arxiv.org/pdf/2405.01469), more details are [here](#pretrained-backbone-xray-dino)* :new: [2025-12-16] *Added Channel-Adaptive DINO code following [Scaling Channel-Adaptive Self-Supervised Learning](https://openreview.net/forum?id=pT8sgtRVAf), more details are [here](#dinov2-for-biology)* :new: [2025-12-16] *Added Cell-DINO code following [Cell-DINO: Self-Supervised Image-based Embeddings for Cell Fluorescent Microscopy](to appear in Plos One Computational Biology), more details are [here](#dinov2-for-biology)* [2025-08-14] *Please check out the more recent [DINOv3](https://github.com/facebookresearch/dinov3) effort continuing this line of work.* [2025-06-11] *Added dino.txt inference code, following [DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment](https://arxiv.org/abs/2412.16334).* [2023-10-26] *Added DINOv2 backbones with registers, following [Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588).* # DINOv2: Learning Robust Visual Features without Supervision **[Meta AI Research, FAIR](https://ai.facebook.com/research/)** Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Armand Joulin, Piotr Bojanowski [[`Paper #1`](https://arxiv.org/abs/2304.07193)] [`Paper #2`](https://arxiv.org/abs/2309.16588)] [[`Blog`](https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/)] [[`Demo`](https://dinov2.metademolab.com)] [[`BibTeX`](#citing-dinov2)] PyTorch implementation and pretrained models for DINOv2. For details, see the papers: **[DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)** and **[Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588)**. DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any requirement for fine-tuning. The models were pretrained on a dataset of 142 M images without using any labels or annotations. https://github.com/facebookresearch/dinov2/assets/60359573/f168823e-7922-415a-b429-578badf5c356
Visualization of the three first principal components of the patch features of all frames, mapped to RGB values.
## Pretrained models
model # of
params		 with
registers		 ImageNet
k-NN		 ImageNet
linear		 download
ViT-S/14 distilled 21 M :x: 79.0% 81.1% backbone only
ViT-S/14 distilled 21 M :white_check_mark: 79.1% 80.9% backbone only
ViT-B/14 distilled 86 M :x: 82.1% 84.5% backbone only
ViT-B/14 distilled 86 M :white_check_mark: 82.0% 84.6% backbone only
ViT-L/14 distilled 300 M :x: 83.5% 86.3% backbone only
ViT-L/14 distilled 300 M :white_check_mark: 83.8% 86.7% backbone only
ViT-g/14 1,100 M :x: 83.5% 86.5% backbone only
ViT-g/14 1,100 M :white_check_mark: 83.7% 87.1% backbone only
### Pretrained backbones (via PyTorch Hub) Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install PyTorch (the only required dependency for loading the model). Installing PyTorch with CUDA support is strongly recommended. A corresponding [model card](MODEL_CARD.md) is included in the repository. ```python import torch # DINOv2 dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14') dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14') dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14') dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14') # DINOv2 with registers dinov2_vits14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14_reg') dinov2_vitb14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14_reg') dinov2_vitl14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14_reg') dinov2_vitg14_reg = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14_reg') ``` ### Pretrained backbone: XRay-DINO Request for downloading the model is here: https://ai.meta.com/resources/models-and-libraries/raydino-downloads/ After filling the form, you will get an email with a temporary link. You can either download it using `wget` and indicate the checkpoint path in your local filesystem, or you can directly use the URL from the email in the following code: ```python import torch REPO_DIR = xray_dino_vitl16 = torch.hub.load(REPO_DIR, 'xray_dino_vitl16', source='local', weights=) ``` **License** Model weights are released under the FAIR Noncommercial Research License. See LICENSE_XRAY_DINO_MODEL for additional details. ### Pretrained heads - Image classification
backbone with
registers		 download
ImageNet
ViT-S/14 distilled :x: linear head (1 layer, 4 layers)
ViT-S/14 distilled :white_check_mark: linear head (1 layer, 4 layers)
ViT-B/14 distilled :x: linear head (1 layer, 4 layers)
ViT-B/14 distilled :white_check_mark: linear head (1 layer, 4 layers)
ViT-L/14 distilled :x: linear head (1 layer, 4 layers)
ViT-L/14 distilled :white_check_mark: linear head (1 layer, 4 layers)
ViT-g/14 :x: linear head (1 layer, 4 layers)
ViT-g/14 :white_check_mark: linear head (1 layer, 4 layers)
The (full) classifier models can be loaded via PyTorch Hub: ```python import torch # DINOv2 dinov2_vits14_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14_lc') dinov2_vitb14_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14_lc') dinov2_vitl14_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14_lc') dinov2_vitg14_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14_lc') # DINOv2 with registers dinov2_vits14_reg_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14_reg_lc') dinov2_vitb14_reg_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14_reg_lc') dinov2_vitl14_reg_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14_reg_lc') dinov2_vitg14_reg_lc = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14_reg_lc') ``` ### Pretrained heads - Depth estimation
backbone download head
NYUd KITTI
ViT-S/14 distilled linear (1 layer, 4 layers), DPT linear (1 layer, 4 layers), DPT
ViT-B/14 distilled linear (1 layer, 4 layers), DPT linear (1 layer, 4 layers), DPT
ViT-L/14 distilled linear (1 layer, 4 layers), DPT linear (1 layer, 4 layers), DPT
ViT-g/14 linear (1 layer, 4 layers), DPT linear (1 layer, 4 layers), DPT
### Pretrained heads - Semantic segmentation
backbone download model download head
ADE20K ADE20K VOC2012
ViT-S/14 distilled linear, multi-scale linear, multi-scale
ViT-B/14 distilled linear, multi-scale linear, multi-scale
ViT-L/14 distilled linear, multi-scale linear, multi-scale
ViT-g/14 Mask2Former linear, multi-scale linear, multi-scale
### Pretrained heads - Zero-shot tasks with dino.txt
backbone with
registers		 download
ViT-L/14 distilled :white_check_mark: vision head, text model, vocabulary, vocabulary license
The (full) dino.txt model can be loaded via PyTorch Hub: ```python import torch # DINOv2 dinov2_vitl14_reg4_dinotxt_tet1280d20h24l = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14_reg4_dinotxt_tet1280d20h24l') ``` ## Installation The training and evaluation code requires PyTorch 2.0 and [xFormers](https://github.com/facebookresearch/xformers) 0.0.18 as well as a number of other 3rd party packages. Note that the code has only been tested with the specified versions and also expects a Linux environment. To setup all the required dependencies for training and evaluation, please follow the instructions below: *[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html)* **(Recommended)** - Clone the repository and then create and activate a `dinov2` conda environment using the provided environment definition: ```shell conda env create -f conda.yaml conda activate dinov2 ``` *[pip](https://pip.pypa.io/en/stable/getting-started/)* - Clone the repository and then use the provided `requirements.txt` to install the dependencies: ```shell pip install -r requirements.txt ``` For dense tasks (depth estimation and semantic segmentation), there are additional dependencies (specific versions of `mmcv` and `mmsegmentation`) which are captured in the `extras` dependency specifications: *[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html)* **(Recommended)**: ```shell conda env create -f conda-extras.yaml conda activate dinov2-extras ``` *[pip](https://pip.pypa.io/en/stable/getting-started/)*: ```shell pip install -r requirements.txt -r requirements-extras.txt ``` ## Data preparation ### ImageNet-1k The root directory of the dataset should hold the following contents: - `/test/ILSVRC2012_test_00000001.JPEG` - `/test/[..]` - `/test/ILSVRC2012_test_00100000.JPEG` - `/train/n01440764/n01440764_10026.JPEG` - `/train/[...]` - `/train/n15075141/n15075141_9993.JPEG` - `/val/n01440764/ILSVRC2012_val_00000293.JPEG` - `/val/[...]` - `/val/n15075141/ILSVRC2012_val_00049174.JPEG` - `/labels.txt` The provided dataset implementation expects a few additional metadata files to be present under the extra directory: - `/class-ids-TRAIN.npy` - `/class-ids-VAL.npy` - `/class-names-TRAIN.npy` - `/class-names-VAL.npy` - `/entries-TEST.npy` - `/entries-TRAIN.npy` - `/entries-VAL.npy` These metadata files can be generated (once) with the following lines of Python code: ```python from dinov2.data.datasets import ImageNet for split in ImageNet.Split: dataset = ImageNet(split=split, root="", extra="") dataset.dump_extra() ``` Note that the root and extra directories do not have to be distinct directories. ### ImageNet-22k Please adapt the [dataset class](dinov2/data/datasets/image_net_22k.py) to match your local setup.
:warning: To execute the commands provided in the next sections for training and evaluation, the `dinov2` package should be included in the Python module search path, i.e. simply prefix the command to run with `PYTHONPATH=.`. ## Training ### Fast setup: training DINOv2 ViT-L/16 on ImageNet-1k Run DINOv2 training on 4 A100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit: ```shell python dinov2/run/train/train.py \ --nodes 4 \ --config-file dinov2/configs/train/vitl16_short.yaml \ --output-dir \ train.dataset_path=ImageNet:split=TRAIN:root=:extra= ``` Training time is approximately 1 day and the resulting checkpoint should reach 81.6% on k-NN eval and 82.9% on linear eval. The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation. ### Long setup: training DINOv2 ViT-L/14 on ImageNet-22k Run DINOv2 training on 12 A100-80GB nodes (96 GPUs) in a SLURM cluster environment with submitit: ```shell python dinov2/run/train/train.py \ --nodes 12 \ --config-file dinov2/configs/train/vitl14.yaml \ --output-dir \ train.dataset_path=ImageNet22k:root=:extra= ``` Training time is approximately 3.3 days and the resulting checkpoint should reach 82.0% on k-NN eval and 84.5% on linear eval. The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation. ## Evaluation The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node: ### k-NN classification on ImageNet-1k ```shell python dinov2/run/eval/knn.py \ --config-file /config.yaml \ --pretrained-weights /eval/training_24999/teacher_checkpoint.pth \ --output-dir /eval/training_24999/knn \ --train-dataset ImageNet:split=TRAIN:root=:extra= \ --val-dataset ImageNet:split=VAL:root=:extra= ``` ### Logistic regression classification on ImageNet-1k ```shell python dinov2/run/eval/log_regression.py \ --config-file /config.yaml \ --pretrained-weights /eval/training_24999/teacher_checkpoint.pth \ --output-dir /eval/training_24999/logreg \ --train-dataset ImageNet:split=TRAIN:root=:extra= \ --val-dataset ImageNet:split=VAL:root=:extra= ``` ### Linear classification with data augmentation on ImageNet-1k ```shell python dinov2/run/eval/linear.py \ --config-file /config.yaml \ --pretrained-weights /eval/training_24999/teacher_checkpoint.pth \ --output-dir /eval/training_24999/linear \ --train-dataset ImageNet:split=TRAIN:root=:extra= \ --val-dataset ImageNet:split=VAL:root=:extra= ``` We release the weights from evaluating the different models:
model with
registers		 ImageNet
top-1		 linear evaluation
ViT-S/14 distilled :x: 81.1% linear head weights
ViT-S/14 distilled :white_check_mark: 80.8% linear head weights
ViT-B/14 distilled :x: 84.5% linear head weights
ViT-B/14 distilled :white_check_mark: 84.4% linear head weights
ViT-L/14 distilled :x: 86.3% linear head weights
ViT-L/14 distilled :white_check_mark: 86.5% linear head weights
ViT-g/14 :x: 86.5% linear head weights
ViT-g/14 :white_check_mark: 87.0% linear head weights
The performance of the provided pretrained model weights can be evaluated as follows on ImageNet-1k: ```shell python dinov2/run/eval/linear.py \ --config-file dinov2/configs/eval/vitg14_pretrain.yaml \ --pretrained-weights https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth \ --train-dataset ImageNet:split=TRAIN:root=:extra= \ --val-dataset ImageNet:split=VAL:root=:extra= ``` ## Notebooks A few notebooks are provided to help the community leverage the models and code:
  • Depth estimation - How to load and use the depth heads in combination with a matching backbone via mmcv
  • Semantic segmentation - How to load and use the segmentation heads in combination with a matching backbone via mmcv, and also how to load and use the Mask2Former-based segmentation model trained on ADE20K
## License DINOv2 code and model weights are released under the Apache License 2.0. See [LICENSE](LICENSE) for additional details. ## Contributing See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md). ## Citing DINOv2 If you find this repository useful, please consider giving a star :star: and citation :t-rex:: ``` @misc{oquab2023dinov2, title={DINOv2: Learning Robust Visual Features without Supervision}, author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr}, journal={arXiv:2304.07193}, year={2023} } ``` ``` @misc{darcet2023vitneedreg, title={Vision Transformers Need Registers}, author={Darcet, Timothée and Oquab, Maxime and Mairal, Julien and Bojanowski, Piotr}, journal={arXiv:2309.16588}, year={2023} } ``` ``` @misc{jose2024dinov2meetstextunified, title={DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment}, author={Cijo Jose and Théo Moutakanni and Dahyun Kang and Federico Baldassarre and Timothée Darcet and Hu Xu and Daniel Li and Marc Szafraniec and Michaël Ramamonjisoa and Maxime Oquab and Oriane Siméoni and Huy V. Vo and Patrick Labatut and Piotr Bojanowski}, journal={arXiv:2412.16334}, year={2024} } ``` # DINOv2 for Biology The contents of the source code contained in the cell_dino folders, including the code and model weights, are intended for research use only. It is not for use in medical procedures, including any diagnostics, treatment, or curative applications. Do not use this model for any clinical purpose or as a substitute for professional medical judgement. ## Scaling Channel-Adaptive Self-Supervised Learning (Channel-Adaptive DINO) [[`Paper `](https://openreview.net/forum?id=pT8sgtRVAf))] [[`BibTeX`](#citing-channeladaptivedino-and-dinov2)] Alice V. De Lorenci, Seungeun Yi, Théo Moutakanni, Piotr Bojanowski, Camille Couprie, Juan C. Caicedo, Wolfgang M. Pernice, with special thanks to Elouan Gardes for his contributions to the codebase. [README](https://github.com/facebookresearch/dinov2/blob/main/docs/README_CHANNEL_ADAPTIVE_DINO.md) ## Cell-DINO: Self-Supervised Image-based Embeddings for Cell Fluorescent Microscopy (Cell-DINO) Théo Moutakanni, Camille Couprie, Seungeun Yi, Elouan Gardes, Piotr Bojanowski, Hugo Touvron, Michael Doron, Zitong S. Chen, Nikita Moshkov, Mathilde Caron, Armand Joulin, Wolfgang M. Pernice, Juan C. Caicedo to appear soon. [README](https://github.com/facebookresearch/dinov2/blob/main/docs/README_CELL_DINO.md) ## Pretrained models ℹ️ Please follow the link provided below to get access to all the model weights: once accepted, an e-mail will be sent with the complete list of URLs pointing to all the available model weights. These URLs can then be used to either: - download the model or adapter weights to a local filesystem and point `torch.hub.load()` to these local weights via the `pretrained_path` parameters, or - directly invoke `torch.hub.load()` to download and load a backbone from its URL via also the `pretrained_url` parameter. ⚠️ Please use wget instead of a web browser to download the weights. **Download link:** https://ai.meta.com/resources/models-and-libraries/cell-dino-downloads/ ```python import torch REPO_DIR = # You can either download the URL link first, then load: cell_dino_vits8 = torch.hub.load(REPO_DIR, 'cell_dino_cp_vits8', source='local', pretrained_path=) # Or directly download the URL while using `torch.hub.load`: cell_dino_vits8 = torch.hub.load(REPO_DIR, 'cell_dino_cp_vits8', source='local', pretrained_url=) # Similarily for other models: cell_dino_vitl16_hpa_sc = torch.hub.load(REPO_DIR, 'cell_dino_hpa_vitl16', source='local', pretrained_path=) cell_dino_vitl16_hpa_fov = torch.hub.load(REPO_DIR, 'cell_dino_hpa_vitl16', source='local', pretrained_path=) channel_adaptive_dino_vitl16 = torch.hub.load(REPO_DIR, 'channel_adaptive_dino_vitl16', source='local', pretrained_path=) cell_dino_vitl14 = torch.hub.load(REPO_DIR, 'cell_dino_hpa_vitl14', source='local', pretrained_path=) ``` ## Licenses Code is released under the CC BY NC License. See [LICENSE_CELL_DINO_CODE](LICENSE_CELL_DINO_CODE) for additional details. Model weights are released under the FAIR Noncommercial Research License. See [LICENSE_CELL_DINO_CODE_WEIGHTS](LICENSE_CELL_DINO_CODE_WEIGHTS) for additional details. ================================================ FILE: conda-extras.yaml ================================================ name: dinov2-extras channels: - defaults - pytorch - nvidia - xformers - conda-forge dependencies: - python=3.9 - pytorch::pytorch=2.0.0 - pytorch::pytorch-cuda=11.7.0 - pytorch::torchvision=0.15.0 - omegaconf - torchmetrics=0.10.3 - fvcore - iopath - xformers::xformers=0.0.18 - pip - pip: - git+https://github.com/facebookincubator/submitit - --extra-index-url https://pypi.nvidia.com - cuml-cu11 - mmcv-full==1.5.0 - mmsegmentation==0.27.0 ================================================ FILE: conda.yaml ================================================ name: dinov2 channels: - defaults - pytorch - nvidia - xformers - conda-forge dependencies: - python=3.9 - pytorch::pytorch=2.0.0 - pytorch::pytorch-cuda=11.7.0 - pytorch::torchvision=0.15.0 - omegaconf - torchmetrics=0.10.3 - fvcore - iopath - xformers::xformers=0.0.18 - pip - pip: - git+https://github.com/facebookincubator/submitit - --extra-index-url https://pypi.nvidia.com - cuml-cu11 ================================================ FILE: dinov2/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. __version__ = "0.0.1" ================================================ FILE: dinov2/configs/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import pathlib from omegaconf import OmegaConf def load_config(config_name: str): config_filename = config_name + ".yaml" return OmegaConf.load(pathlib.Path(__file__).parent.resolve() / config_filename) dinov2_default_config = load_config("ssl_default_config") def load_and_merge_config(config_name: str): default_config = OmegaConf.create(dinov2_default_config) loaded_config = load_config(config_name) return OmegaConf.merge(default_config, loaded_config) ================================================ FILE: dinov2/configs/eval/cell_dino/vitl16_channel_adaptive_pretrain.yaml ================================================ train: batch_size_per_gpu: 32 OFFICIAL_EPOCH_LENGTH: 450 cell_augmentation: true channel_adaptive: true student: arch: vit_large patch_size: 16 num_register_tokens: 0 interpolate_antialias: false interpolate_offset: 0.1 drop_path_rate: 0.1 in_chans: 1 block_chunks: 4 channel_adaptive: true teacher: momentum_teacher: 0.996 warmup_teacher_temp_epochs: 20 in_chans: 1 channel_adaptive: true crops: global_crops_scale: - 0.4 - 1.0 local_crops_number: 8 local_crops_scale: - 0.005 - 0.4 global_crops_size: 224 local_crops_size: 96 optim: weight_decay_end: 0.2 base_lr: 5.0e-4 warmup_epochs: 20 epochs: 400 ================================================ FILE: dinov2/configs/eval/cell_dino/vitl16_pretrain.yaml ================================================ student: arch: vit_large patch_size: 16 num_register_tokens: 0 interpolate_antialias: false interpolate_offset: 0.1 drop_path_rate: 0.1 in_chans: 4 block_chunks: 4 teacher: in_chans: 4 crops: global_crops_size: 224 local_crops_size: 96 ================================================ FILE: dinov2/configs/eval/vitb14_pretrain.yaml ================================================ student: arch: vit_base patch_size: 14 crops: global_crops_size: 518 # this is to set up the position embeddings properly local_crops_size: 98 ================================================ FILE: dinov2/configs/eval/vitb14_reg4_pretrain.yaml ================================================ student: arch: vit_base patch_size: 14 num_register_tokens: 4 interpolate_antialias: true interpolate_offset: 0.0 crops: global_crops_size: 518 # this is to set up the position embeddings properly local_crops_size: 98 ================================================ FILE: dinov2/configs/eval/vitg14_pretrain.yaml ================================================ student: arch: vit_giant2 patch_size: 14 ffn_layer: swiglufused crops: global_crops_size: 518 # this is to set up the position embeddings properly local_crops_size: 98 ================================================ FILE: dinov2/configs/eval/vitg14_reg4_pretrain.yaml ================================================ student: arch: vit_giant2 patch_size: 14 ffn_layer: swiglufused num_register_tokens: 4 interpolate_antialias: true interpolate_offset: 0.0 crops: global_crops_size: 518 # this is to set up the position embeddings properly local_crops_size: 98 ================================================ FILE: dinov2/configs/eval/vitl14_pretrain.yaml ================================================ student: arch: vit_large patch_size: 14 crops: global_crops_size: 518 # this is to set up the position embeddings properly local_crops_size: 98 ================================================ FILE: dinov2/configs/eval/vitl14_reg4_pretrain.yaml ================================================ student: arch: vit_large patch_size: 14 num_register_tokens: 4 interpolate_antialias: true interpolate_offset: 0.0 crops: global_crops_size: 518 # this is to set up the position embeddings properly local_crops_size: 98 ================================================ FILE: dinov2/configs/eval/vits14_pretrain.yaml ================================================ student: arch: vit_small patch_size: 14 crops: global_crops_size: 518 # this is to set up the position embeddings properly local_crops_size: 98 ================================================ FILE: dinov2/configs/eval/vits14_reg4_pretrain.yaml ================================================ student: arch: vit_small patch_size: 14 num_register_tokens: 4 interpolate_antialias: true interpolate_offset: 0.0 crops: global_crops_size: 518 # this is to set up the position embeddings properly local_crops_size: 98 ================================================ FILE: dinov2/configs/ssl_default_config.yaml ================================================ MODEL: WEIGHTS: '' compute_precision: grad_scaler: true teacher: backbone: sharding_strategy: SHARD_GRAD_OP mixed_precision: param_dtype: fp16 reduce_dtype: fp16 buffer_dtype: fp32 dino_head: sharding_strategy: SHARD_GRAD_OP mixed_precision: param_dtype: fp16 reduce_dtype: fp16 buffer_dtype: fp32 ibot_head: sharding_strategy: SHARD_GRAD_OP mixed_precision: param_dtype: fp16 reduce_dtype: fp16 buffer_dtype: fp32 student: backbone: sharding_strategy: SHARD_GRAD_OP mixed_precision: param_dtype: fp16 reduce_dtype: fp16 buffer_dtype: fp32 dino_head: sharding_strategy: SHARD_GRAD_OP mixed_precision: param_dtype: fp16 reduce_dtype: fp32 buffer_dtype: fp32 ibot_head: sharding_strategy: SHARD_GRAD_OP mixed_precision: param_dtype: fp16 reduce_dtype: fp32 buffer_dtype: fp32 dino: loss_weight: 1.0 head_n_prototypes: 65536 head_bottleneck_dim: 256 head_nlayers: 3 head_hidden_dim: 2048 koleo_loss_weight: 0.1 ibot: loss_weight: 1.0 mask_sample_probability: 0.5 mask_ratio_min_max: - 0.1 - 0.5 separate_head: false head_n_prototypes: 65536 head_bottleneck_dim: 256 head_nlayers: 3 head_hidden_dim: 2048 train: batch_size_per_gpu: 64 dataset_path: ImageNet:split=TRAIN output_dir: . saveckp_freq: 20 seed: 0 num_workers: 10 OFFICIAL_EPOCH_LENGTH: 1250 cache_dataset: true centering: "centering" # or "sinkhorn_knopp" cell_augmentation: false student: arch: vit_large patch_size: 16 drop_path_rate: 0.3 layerscale: 1.0e-05 drop_path_uniform: true pretrained_weights: '' ffn_layer: "mlp" block_chunks: 0 qkv_bias: true proj_bias: true ffn_bias: true num_register_tokens: 0 interpolate_antialias: false interpolate_offset: 0.1 in_chans: 3 channel_adaptive: false teacher: momentum_teacher: 0.992 final_momentum_teacher: 1 warmup_teacher_temp: 0.04 teacher_temp: 0.07 warmup_teacher_temp_epochs: 30 in_chans: 3 channel_adaptive: false optim: epochs: 100 weight_decay: 0.04 weight_decay_end: 0.4 base_lr: 0.004 # learning rate for a batch size of 1024 lr: 0. # will be set after applying scaling rule warmup_epochs: 10 min_lr: 1.0e-06 clip_grad: 3.0 freeze_last_layer_epochs: 1 scaling_rule: sqrt_wrt_1024 patch_embed_lr_mult: 0.2 layerwise_decay: 0.9 adamw_beta1: 0.9 adamw_beta2: 0.999 crops: global_crops_scale: - 0.32 - 1.0 local_crops_number: 8 local_crops_scale: - 0.05 - 0.32 global_crops_size: 224 local_crops_size: 96 evaluation: eval_period_iterations: 12500 ================================================ FILE: dinov2/configs/train/cell_dino/vitl16_boc_hpafov.yaml ================================================ train: batch_size_per_gpu: 16 OFFICIAL_EPOCH_LENGTH: 450 cell_augmentation: true channel_adaptive: true student: arch: vit_large patch_size: 16 in_chans: 1 drop_path_rate: 0.1 block_chunks: 4 teacher: momentum_teacher: 0.996 warmup_teacher_temp_epochs: 20 in_chans: 1 crops: global_crops_scale: - 0.4 - 1.0 local_crops_number: 8 local_crops_scale: - 0.005 - 0.4 global_crops_size: 224 local_crops_size: 96 optim: weight_decay_end: 0.2 base_lr: 5.0e-4 warmup_epochs: 20 epochs: 400 ================================================ FILE: dinov2/configs/train/cell_dino/vitl16_hpafov.yaml ================================================ train: batch_size_per_gpu: 16 OFFICIAL_EPOCH_LENGTH: 450 cell_augmentation: true student: arch: vit_large patch_size: 16 in_chans: 4 drop_path_rate: 0.1 block_chunks: 4 teacher: momentum_teacher: 0.996 warmup_teacher_temp_epochs: 20 in_chans: 4 optim: weight_decay_end: 0.2 base_lr: 5.0e-4 warmup_epochs: 20 crops: global_crops_scale: - 0.4 - 1.0 local_crops_number: 8 local_crops_scale: - 0.005 - 0.4 global_crops_size: 224 local_crops_size: 96 evaluation: eval_period_iterations: 9000 ================================================ FILE: dinov2/configs/train/cell_dino/vitl16_hpaone.yaml ================================================ train: batch_size_per_gpu: 16 OFFICIAL_EPOCH_LENGTH: 1756 cell_augmentation: true student: arch: vit_large patch_size: 16 in_chans: 4 drop_path_rate: 0.1 block_chunks: 4 teacher: momentum_teacher: 0.996 warmup_teacher_temp_epochs: 20 in_chans: 4 optim: weight_decay_end: 0.2 base_lr: 5.0e-4 warmup_epochs: 20 crops: global_crops_scale: - 0.4 - 1.0 local_crops_number: 8 local_crops_scale: - 0.005 - 0.4 global_crops_size: 224 local_crops_size: 96 evaluation: eval_period_iterations: 9000 ================================================ FILE: dinov2/configs/train/vitg14.yaml ================================================ dino: head_n_prototypes: 131072 head_bottleneck_dim: 384 ibot: separate_head: true head_n_prototypes: 131072 train: batch_size_per_gpu: 12 dataset_path: ImageNet22k centering: sinkhorn_knopp student: arch: vit_giant2 patch_size: 14 drop_path_rate: 0.4 ffn_layer: swiglufused block_chunks: 4 teacher: momentum_teacher: 0.994 optim: epochs: 500 weight_decay_end: 0.2 base_lr: 2.0e-04 # learning rate for a batch size of 1024 warmup_epochs: 80 layerwise_decay: 1.0 crops: local_crops_size: 98 ================================================ FILE: dinov2/configs/train/vitl14.yaml ================================================ dino: head_n_prototypes: 131072 head_bottleneck_dim: 384 ibot: separate_head: true head_n_prototypes: 131072 train: batch_size_per_gpu: 32 dataset_path: ImageNet22k centering: sinkhorn_knopp student: arch: vit_large patch_size: 14 drop_path_rate: 0.4 ffn_layer: swiglufused block_chunks: 4 teacher: momentum_teacher: 0.994 optim: epochs: 500 weight_decay_end: 0.2 base_lr: 2.0e-04 # learning rate for a batch size of 1024 warmup_epochs: 80 layerwise_decay: 1.0 crops: local_crops_size: 98 ================================================ FILE: dinov2/configs/train/vitl16_short.yaml ================================================ # this corresponds to the default config train: dataset_path: ImageNet:split=TRAIN batch_size_per_gpu: 64 student: block_chunks: 4 ================================================ FILE: dinov2/data/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .adapters import DatasetWithEnumeratedTargets from .loaders import make_data_loader, make_dataset, SamplerType from .collate import collate_data_and_cast from .masking import MaskingGenerator from .augmentations import DataAugmentationDINO from .cell_dino.augmentations import CellAugmentationDINO from .accumulators import NoOpAccumulator, ResultsAccumulator ================================================ FILE: dinov2/data/accumulators.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from collections import defaultdict from typing import Dict, List, Optional, Any import torch from torch import Tensor from torch.nn import functional as F import torch.distributed as dist from dinov2.distributed import get_global_size def _simple_gather_all_tensors(result: torch.Tensor, group: Any, world_size: int) -> List[torch.Tensor]: gathered_result = [torch.zeros_like(result) for _ in range(world_size)] dist.all_gather(gathered_result, result, group) return gathered_result def gather_all_tensors(result: torch.Tensor, group: Optional[Any] = None) -> List[torch.Tensor]: """ Copied from https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/utilities/distributed.py Gather all tensors from several ddp processes onto a list that is broadcasted to all processes. Works on tensors that have the same number of dimensions, but where each dimension may differ. In this case tensors are padded, gathered and then trimmed to secure equal workload for all processes. Args: result: the value to sync group: the process group to gather results from. Defaults to all processes (world) Return: list with size equal to the process group where element i corresponds to result tensor from process i """ # convert tensors to contiguous format result = result.contiguous() world_size = get_global_size() dist.barrier(group=group) # if the tensor is scalar, things are easy if result.ndim == 0: return _simple_gather_all_tensors(result, group, world_size) # 1. Gather sizes of all tensors local_size = torch.tensor(result.shape, device=result.device) local_sizes = [torch.zeros_like(local_size) for _ in range(world_size)] dist.all_gather(local_sizes, local_size, group=group) max_size = torch.stack(local_sizes).max(dim=0).values all_sizes_equal = all(all(ls == max_size) for ls in local_sizes) # 2. If shapes are all the same, then do a simple gather: if all_sizes_equal: return _simple_gather_all_tensors(result, group, world_size) # 3. If not, we need to pad each local tensor to maximum size, gather and then truncate pad_dims = [] pad_by = (max_size - local_size).detach().cpu() for val in reversed(pad_by): pad_dims.append(0) pad_dims.append(val.item()) result_padded = F.pad(result, pad_dims) gathered_result = [torch.zeros_like(result_padded) for _ in range(world_size)] dist.all_gather(gathered_result, result_padded, group) for idx, item_size in enumerate(local_sizes): slice_param = [slice(dim_size) for dim_size in item_size] gathered_result[idx] = gathered_result[idx][slice_param] return gathered_result def _cat_and_gather_tensor_list(tensor_list: List[Tensor]) -> Tensor: local_cat = torch.cat(tensor_list) return torch.cat(gather_all_tensors(local_cat)) class Accumulator: def __init__(self) -> None: pass def update(self, preds: Tensor, target: Tensor, index: Tensor) -> None: raise NotImplementedError def accumulate(self) -> Optional[Dict[str, Tensor]]: raise NotImplementedError class NoOpAccumulator(Accumulator): def __init__(self) -> None: pass def update(self, preds: Tensor, target: Tensor, index: Tensor) -> None: pass def accumulate(self) -> None: return None class ResultsAccumulator(Accumulator): """ Accumulate predictions and targets across processes """ def __init__(self) -> None: self._local_values: Dict[str, List[Tensor]] = defaultdict(list) self._gathered_values: Dict[str, Tensor] = {} self._gathered = False def update(self, preds: Tensor, target: Tensor, index: Tensor) -> None: assert len(preds) == len(target) == len(index) assert not self._gathered, "Tensors have already been gathered in this helper" self._local_values["preds"].append(preds) self._local_values["target"].append(target) self._local_values["index"].append(index) self._gathered = False def _gather_tensors(self): for k, tensor_list in self._local_values.items(): self._gathered_values[k] = _cat_and_gather_tensor_list(tensor_list) self._gathered = True def accumulate(self) -> Dict[str, Tensor]: if not self._gathered: self._gather_tensors() preds, target, index = [self._gathered_values[k] for k in ["preds", "target", "index"]] assert len(preds) == len(target) == len(index) and index.min() == 0 preds_ordered = torch.zeros((index.max() + 1, *preds.shape[1:]), dtype=preds.dtype, device=preds.device) preds_ordered[index] = preds target_ordered = torch.zeros((index.max() + 1, *target.shape[1:]), dtype=target.dtype, device=target.device) target_ordered[index] = target return {"preds": preds_ordered, "target": target_ordered} ================================================ FILE: dinov2/data/adapters.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from typing import Any, Tuple, Optional from torch.utils.data import Dataset class DatasetWithEnumeratedTargets(Dataset): """ If pad_dataset is set, pads based on torch's DistributedSampler implementation, which with drop_last=False pads the last batch to be a multiple of the world size. https://github.com/pytorch/pytorch/blob/main/torch/utils/data/distributed.py#L91 """ def __init__(self, dataset: Dataset, pad_dataset: bool = False, num_replicas: Optional[int] = None): self._dataset = dataset self._size = len(self._dataset) self._padded_size = self._size self._pad_dataset = pad_dataset if self._pad_dataset: assert num_replicas is not None, "num_replicas should be set if pad_dataset is True" self._padded_size = num_replicas * ((len(dataset) + num_replicas - 1) // num_replicas) def get_image_relpath(self, index: int) -> str: assert self._pad_dataset or index < self._size return self._dataset.get_image_relpath(index % self._size) def get_image_data(self, index: int) -> bytes: assert self._pad_dataset or index < self._size return self._dataset.get_image_data(index % self._size) def get_target(self, index: int) -> Tuple[Any, int]: target = self._dataset.get_target(index % self._size) if index >= self._size: assert self._pad_dataset return (-1, target) return (index, target) def __getitem__(self, index: int) -> Tuple[Any, Tuple[Any, int]]: image, target = self._dataset[index % self._size] if index >= self._size: assert self._pad_dataset return image, (-1, target) target = index if target is None else target return image, (index, target) def __len__(self) -> int: return self._padded_size ================================================ FILE: dinov2/data/augmentations.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging from torchvision import transforms from .transforms import ( GaussianBlur, make_normalize_transform, ) logger = logging.getLogger("dinov2") class DataAugmentationDINO(object): def __init__( self, global_crops_scale, local_crops_scale, local_crops_number, global_crops_size=224, local_crops_size=96, ): self.global_crops_scale = global_crops_scale self.local_crops_scale = local_crops_scale self.local_crops_number = local_crops_number self.global_crops_size = global_crops_size self.local_crops_size = local_crops_size logger.info("###################################") logger.info("Using data augmentation parameters:") logger.info(f"global_crops_scale: {global_crops_scale}") logger.info(f"local_crops_scale: {local_crops_scale}") logger.info(f"local_crops_number: {local_crops_number}") logger.info(f"global_crops_size: {global_crops_size}") logger.info(f"local_crops_size: {local_crops_size}") logger.info("###################################") # random resized crop and flip self.geometric_augmentation_global = transforms.Compose( [ transforms.RandomResizedCrop( global_crops_size, scale=global_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC ), transforms.RandomHorizontalFlip(p=0.5), ] ) self.geometric_augmentation_local = transforms.Compose( [ transforms.RandomResizedCrop( local_crops_size, scale=local_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC ), transforms.RandomHorizontalFlip(p=0.5), ] ) # color distorsions / blurring color_jittering = transforms.Compose( [ transforms.RandomApply( [transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)], p=0.8, ), transforms.RandomGrayscale(p=0.2), ] ) global_transfo1_extra = GaussianBlur(p=1.0) global_transfo2_extra = transforms.Compose( [ GaussianBlur(p=0.1), transforms.RandomSolarize(threshold=128, p=0.2), ] ) local_transfo_extra = GaussianBlur(p=0.5) # normalization self.normalize = transforms.Compose( [ transforms.ToTensor(), make_normalize_transform(), ] ) self.global_transfo1 = transforms.Compose([color_jittering, global_transfo1_extra, self.normalize]) self.global_transfo2 = transforms.Compose([color_jittering, global_transfo2_extra, self.normalize]) self.local_transfo = transforms.Compose([color_jittering, local_transfo_extra, self.normalize]) def __call__(self, image): output = {} # global crops: im1_base = self.geometric_augmentation_global(image) global_crop_1 = self.global_transfo1(im1_base) im2_base = self.geometric_augmentation_global(image) global_crop_2 = self.global_transfo2(im2_base) output["global_crops"] = [global_crop_1, global_crop_2] # global crops for teacher: output["global_crops_teacher"] = [global_crop_1, global_crop_2] # local crops: local_crops = [ self.local_transfo(self.geometric_augmentation_local(image)) for _ in range(self.local_crops_number) ] output["local_crops"] = local_crops output["offsets"] = () return output ================================================ FILE: dinov2/data/cell_dino/augmentations.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import logging import torchvision from torchvision import transforms from .transforms import ( RandomContrastProteinChannel, RandomRemoveChannelExceptProtein, RandomBrightness, RandomContrast, Div255, SelfNormalizeNoDiv, ) logger = logging.getLogger("dinov2") class CellAugmentationDINO(object): def __init__( self, global_crops_scale, local_crops_scale, local_crops_number, global_crops_size=224, local_crops_size=96, ): self.global_crops_scale = global_crops_scale self.local_crops_scale = local_crops_scale self.local_crops_number = local_crops_number self.global_crops_size = global_crops_size self.local_crops_size = local_crops_size logger.info("###################################") logger.info("Using data augmentation parameters:") logger.info(f"global_crops_scale: {global_crops_scale}") logger.info(f"local_crops_scale: {local_crops_scale}") logger.info(f"local_crops_number: {local_crops_number}") logger.info(f"global_crops_size: {global_crops_size}") logger.info(f"local_crops_size: {local_crops_size}") logger.info("###################################") additional_transforms_list = [ torchvision.transforms.RandomHorizontalFlip(), torchvision.transforms.RandomVerticalFlip(), RandomBrightness(), RandomContrast(), SelfNormalizeNoDiv(), ] first_transforms_list = [ Div255(), RandomRemoveChannelExceptProtein(), RandomContrastProteinChannel(), ] global_transforms_list = first_transforms_list.copy() global_transforms_list.append( torchvision.transforms.RandomResizedCrop(size=global_crops_size, scale=global_crops_scale) ) global_transforms_list = global_transforms_list + additional_transforms_list local_transforms_list = first_transforms_list local_transforms_list.append( torchvision.transforms.RandomResizedCrop(size=local_crops_size, scale=local_crops_scale) ) local_transforms_list = local_transforms_list + additional_transforms_list self.global_transform = transforms.Compose(global_transforms_list) self.local_transform = transforms.Compose(local_transforms_list) def __call__(self, image): output = {} global_crop1 = self.global_transform(image) global_crop2 = self.global_transform(image) output["global_crops"] = [global_crop1, global_crop2] local_crops = [] for _ in range(self.local_crops_number): local_crops.append(self.local_transform(image)) output["local_crops"] = local_crops output["global_crops_teacher"] = [global_crop1, global_crop2] output["offsets"] = () return output ================================================ FILE: dinov2/data/cell_dino/transforms.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import torch from torchvision import transforms import numpy as np from enum import Enum class NormalizationType(Enum): SELF_NORM_AUG_DECODER = "self_norm_aug_decoder" SELF_NORM_CENTER_CROP = "self_norm_center_crop" class Div255(torch.nn.Module): def forward(self, x): x = x / 255 return x class SelfNormalizeNoDiv(torch.nn.Module): def forward(self, x): m = x.mean((-2, -1), keepdim=True) s = x.std((-2, -1), unbiased=False, keepdim=True) x -= m x /= s + 1e-7 return x class SelfNormalize(torch.nn.Module): def forward(self, x): x = x / 255 m = x.mean((-2, -1), keepdim=True) s = x.std((-2, -1), unbiased=False, keepdim=True) x -= m x /= s + 1e-7 return x class RandomContrastProteinChannel(torch.nn.Module): """ Random constrast rescaling of the protein channel only. RescaleProtein function in Dino4cell codebase. """ def __init__(self, p=0.2): super().__init__() self.p = p def forward(self, img): if img.max() == 0: return img if len(img) == 1: return img if np.random.rand() <= self.p: random_factor = (np.random.rand() * 2) / img.max() # scaling img[1] = img[1] * random_factor return img else: return img class RandomRemoveChannelExceptProtein(torch.nn.Module): """ dropping a channel at random except the channel 1, corresponding to proteins in HPA datasets. """ def __init__(self, p=0.2): super().__init__() self.p = p def forward(self, img): img_size = np.array(img).shape if img_size[0] < 4: return img if np.random.rand() <= self.p: channel_to_blacken = np.random.choice(np.array([0, 2, 3])) img[channel_to_blacken] = torch.zeros(1, *img.shape[1:]) return img else: return img class RandomRemoveChannel(torch.nn.Module): """ dropping a channel at random """ def __init__(self, p=0.2): super().__init__() self.p = p def forward(self, img): img_size = np.array(img).shape num_channels = img_size[0] if num_channels < 4: return img if np.random.rand() <= self.p: channel_to_blacken = np.random.choice(np.array(list(range(num_channels)))) img[channel_to_blacken] = torch.zeros(1, *img.shape[1:]) return img else: return img class RandomContrast(torch.nn.Module): def __init__(self, p=0.2): super().__init__() self.p = p def forward(self, img): if img.max() == 0: return img n_channels = img.shape[0] for ind in range(n_channels): factor = max(np.random.normal(1, self.p), 0.5) img[ind] = transforms.functional.adjust_contrast(img[ind][None, ...], factor) return img class RandomBrightness(torch.nn.Module): def __init__(self, p=0.2): super().__init__() self.p = p def forward(self, img): if img.max() == 0: return img n_channels = img.shape[0] for ind in range(n_channels): factor = max(np.random.normal(1, self.p), 0.5) img[ind] = transforms.functional.adjust_brightness(img[ind], factor) return img def make_classification_eval_cell_transform( *, resize_size: int = 0, interpolation=transforms.InterpolationMode.BICUBIC, crop_size: int = 384, normalization_type: Enum = NormalizationType.SELF_NORM_CENTER_CROP, ) -> transforms.Compose: from .transforms import ( Div255, SelfNormalizeNoDiv, ) transforms_list = [Div255()] if resize_size > 0: transforms_list.append(transforms.Resize(resize_size, interpolation=interpolation)) if normalization_type == NormalizationType.SELF_NORM_AUG_DECODER: transforms_list.extend( [ transforms.RandomCrop(size=crop_size, pad_if_needed=True), transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), ] ) elif normalization_type == NormalizationType.SELF_NORM_CENTER_CROP: transforms_list.append(transforms.CenterCrop(size=crop_size)) else: raise ValueError("f{normalization_type}: unknown NormalizationType") transforms_list.append(SelfNormalizeNoDiv()) return transforms.Compose(transforms_list) ================================================ FILE: dinov2/data/collate.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import random def collate_data_and_cast(samples_list, mask_ratio_tuple, mask_probability, dtype, n_tokens=None, mask_generator=None): # dtype = torch.half # TODO: Remove n_global_crops = len(samples_list[0][0]["global_crops"]) n_local_crops = len(samples_list[0][0]["local_crops"]) collated_global_crops = torch.stack([s[0]["global_crops"][i] for i in range(n_global_crops) for s in samples_list]) collated_local_crops = torch.stack([s[0]["local_crops"][i] for i in range(n_local_crops) for s in samples_list]) B = len(collated_global_crops) N = n_tokens n_samples_masked = int(B * mask_probability) probs = torch.linspace(*mask_ratio_tuple, n_samples_masked + 1) upperbound = 0 masks_list = [] for i in range(0, n_samples_masked): prob_min = probs[i] prob_max = probs[i + 1] masks_list.append(torch.BoolTensor(mask_generator(int(N * random.uniform(prob_min, prob_max))))) upperbound += int(N * prob_max) for i in range(n_samples_masked, B): masks_list.append(torch.BoolTensor(mask_generator(0))) random.shuffle(masks_list) collated_masks = torch.stack(masks_list).flatten(1) mask_indices_list = collated_masks.flatten().nonzero().flatten() masks_weight = (1 / collated_masks.sum(-1).clamp(min=1.0)).unsqueeze(-1).expand_as(collated_masks)[collated_masks] return { "collated_global_crops": collated_global_crops.to(dtype), "collated_local_crops": collated_local_crops.to(dtype), "collated_masks": collated_masks, "mask_indices_list": mask_indices_list, "masks_weight": masks_weight, "upperbound": upperbound, "n_masked_patches": torch.full((1,), fill_value=mask_indices_list.shape[0], dtype=torch.long), } ================================================ FILE: dinov2/data/datasets/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .image_net import ImageNet from .image_net_22k import ImageNet22k from .cell_dino.hpaone import HPAone from .cell_dino.hpafov import HPAFoV from .cell_dino.chammi_cp import CHAMMI_CP from .cell_dino.chammi_hpa import CHAMMI_HPA from .cell_dino.chammi_wtc import CHAMMI_WTC ================================================ FILE: dinov2/data/datasets/cell_dino/chammi_cp.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import csv from enum import Enum import logging import os from typing import Any, Callable, Optional, Union import numpy as np from ..extended import ExtendedVisionDataset from ..decoders import DecoderType logger = logging.getLogger("dinov2") METADATA_FILE = "morphem70k_v2.csv" CLASS_LABELS = { "BRD-A29260609": 0, "BRD-K04185004": 1, "BRD-K21680192": 2, "DMSO": 3, "BRD-K11129031": 4, # labels only seen in TASK_FOUR "BRD-K62310379": 5, "BRD-K77947974": 6, } class _Split(Enum): TRAIN = "Train" TASK_ONE = "Task_one" TASK_TWO = "Task_two" TASK_THREE = "Task_three" TASK_FOUR = "Task_four" def _load_file_names_and_targets( root: str, split: _Split, ): image_paths = [] labels = [] with open(os.path.join(root, METADATA_FILE)) as metadata: metadata_reader = csv.DictReader(metadata) for row in metadata_reader: row_dataset = row["file_path"].split("/")[0] if row["train_test_split"].upper() == split and row_dataset == "CP": image_paths.append(row["file_path"]) labels.append(CLASS_LABELS[row["label"]]) return image_paths, labels # to debug class CHAMMI_CP(ExtendedVisionDataset): """ Implementation of the CP (Cell-Painting) subset of the CHAMMI benchmark dataset, following the CHAMMI paper: https://arxiv.org/pdf/2310.19224 Github code: https://github.com/chaudatascience/channel_adaptive_models """ Split = Union[_Split] def __init__( self, *, split: "CHAMMI_CP.Split", root: str, transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, image_decoder_type: DecoderType = DecoderType.XChannelsDecoder, **kwargs: Any, ) -> None: super().__init__( root, transforms, transform, target_transform, image_decoder_type=image_decoder_type, **kwargs, ) self.split = split self.root = root self.num_additional_labels_loo_eval = 3 self._image_paths, self._targets = _load_file_names_and_targets( root, split, ) def get_image_relpath(self, index: int) -> str: return self._image_paths[index] def get_image_data(self, index: int) -> bytes: image_relpath = self.get_image_relpath(index) image_full_path = os.path.join(self.root, image_relpath) with open(image_full_path, mode="rb") as f: image_data = f.read() return image_data def get_target(self, index: int) -> Any: return self._targets[index] def get_targets(self) -> np.ndarray: return np.array(self._targets) def __len__(self) -> int: return len(self._image_paths) ================================================ FILE: dinov2/data/datasets/cell_dino/chammi_hpa.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import csv from enum import Enum import logging import os from typing import Any, Callable, Optional, Union import numpy as np from ..extended import ExtendedVisionDataset from ..decoders import DecoderType logger = logging.getLogger("dinov2") METADATA_FILE = "morphem70k_v2.csv" CLASS_LABELS = { "golgi apparatus": 0, "microtubules": 1, "mitochondria": 2, "nuclear speckles": 3, "cytosol": 4, # labels only seen in TASK_THREE "endoplasmic reticulum": 5, "nucleoplasm": 6, } class _Split(Enum): TRAIN = "Train" TASK_ONE = "Task_one" TASK_TWO = "Task_two" TASK_THREE = "Task_three" def _load_file_names_and_targets( root: str, split: _Split, ): image_paths = [] labels = [] with open(os.path.join(root, METADATA_FILE)) as metadata: metadata_reader = csv.DictReader(metadata) for row in metadata_reader: row_dataset = row["file_path"].split("/")[0] if row["train_test_split"].upper() == split and row_dataset == "HPA": image_paths.append(row["file_path"]) labels.append(CLASS_LABELS[row["label"]]) return image_paths, labels class CHAMMI_HPA(ExtendedVisionDataset): """ Implementation of the CP (Cell-Painting) subset of the CHAMMI benchmark dataset, following the CHAMMI paper: https://arxiv.org/pdf/2310.19224 Github code: https://github.com/chaudatascience/channel_adaptive_models """ Split = Union[_Split] def __init__( self, *, split: "CHAMMI_HPA.Split", root: str, transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, image_decoder_type: DecoderType = DecoderType.XChannelsDecoder, **kwargs: Any, ) -> None: super().__init__( root, transforms, transform, target_transform, image_decoder_type=image_decoder_type, **kwargs, ) self.split = split self.root = root self.num_additional_labels_loo_eval = 3 self._image_paths, self._targets = _load_file_names_and_targets( root, split, ) def get_image_relpath(self, index: int) -> str: return self._image_paths[index] def get_image_data(self, index: int) -> bytes: image_relpath = self.get_image_relpath(index) image_full_path = os.path.join(self.root, image_relpath) with open(image_full_path, mode="rb") as f: image_data = f.read() return image_data def get_target(self, index: int) -> Any: return self._targets[index] def get_targets(self) -> np.ndarray: return np.array(self._targets) def __len__(self) -> int: return len(self._image_paths) ================================================ FILE: dinov2/data/datasets/cell_dino/chammi_wtc.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import csv from enum import Enum import logging import os from typing import Any, Callable, Optional, Union import numpy as np from ..extended import ExtendedVisionDataset from ..decoders import DecoderType logger = logging.getLogger("dinov2") METADATA_FILE = "morphem70k_v2.csv" CLASS_LABELS = { "M0": 0, "M1M2": 1, "M3": 2, "M4M5": 3, "M6M7_complete": 4, "M6M7_single": 5, } class _Split(Enum): TRAIN = "Train" TASK_ONE = "Task_one" TASK_TWO = "Task_two" def _load_file_names_and_targets( root: str, split: _Split, ): image_paths = [] labels = [] with open(os.path.join(root, METADATA_FILE)) as metadata: metadata_reader = csv.DictReader(metadata) for row in metadata_reader: row_dataset = row["file_path"].split("/")[0] if row["train_test_split"].upper() == split and row_dataset == "Allen": image_paths.append(row["file_path"]) labels.append(CLASS_LABELS[row["label"]]) return image_paths, labels class CHAMMI_WTC(ExtendedVisionDataset): """ Implementation of the CP (Cell-Painting) subset of the CHAMMI benchmark dataset, following the CHAMMI paper: https://arxiv.org/pdf/2310.19224 Github code: https://github.com/chaudatascience/channel_adaptive_models """ Split = Union[_Split] def __init__( self, *, split: "CHAMMI_WTC.Split", root: str, transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, image_decoder_type: DecoderType = DecoderType.XChannelsTIFFDecoder, **kwargs: Any, ) -> None: super().__init__( root, transforms, transform, target_transform, image_decoder_type=image_decoder_type, **kwargs, ) self.split = split self.root = root self._image_paths, self._targets = _load_file_names_and_targets( root, split, ) def get_image_relpath(self, index: int) -> str: return self._image_paths[index] def get_image_data(self, index: int) -> bytes: image_relpath = self.get_image_relpath(index) image_full_path = os.path.join(self.root, image_relpath) with open(image_full_path, mode="rb") as f: image_data = f.read() return image_data def get_target(self, index: int) -> Any: return self._targets[index] def get_targets(self) -> np.ndarray: return np.array(self._targets) def __len__(self) -> int: return len(self._image_paths) ================================================ FILE: dinov2/data/datasets/cell_dino/hpafov.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import csv from enum import Enum import logging import os from typing import Any, Callable, List, Optional, Tuple, Union, Dict import numpy as np from ..extended import ExtendedVisionDataset from ..decoders import DecoderType logger = logging.getLogger("dinov2") CELL_TYPE = [ "BJ", # 1 "LHCN-M2", "RH-30", "SH-SY5Y", "U-2 OS", # 5 "ASC TERT1", "HaCaT", "A-431", "U-251 MG", "HEK 293", # 10 "A549", "RT4", "HeLa", "MCF7", "PC-3", # 15 "hTERT-RPE1", "SK-MEL-30", "EFO-21", "AF22", "HEL", # 20 "Hep G2", "HUVEC TERT2", "THP-1", "CACO-2", "JURKAT", # 25 "RPTEC TERT1", "SuSa", "REH", "HDLM-2", "K-562", # 30 "hTCEpi", "NB-4", "HAP1", "OE19", "SiHa", # 35 ] PROTEIN_LOCALIZATION = [ # matches https://www.kaggle.com/c/human-protein-atlas-image-classification/data "nucleoplasm", "nuclear membrane", "nucleoli", "nucleoli fibrillar center", "nuclear speckles", # 5 "nuclear bodies", "endoplasmic reticulum", "golgi apparatus", "peroxisomes", "endosomes", # 10 "lysosomes", "intermediate filaments", "actin filaments", "focal adhesion sites", "microtubules", # 15 "microtubule ends", "cytokinetic bridge", "mitotic spindle", "microtubule organizing center", "centrosome", # 20 "lipid droplets", "plasma membrane", "cell junctions", "mitochondria", "aggresome", # 25 "cytosol", "cytoplasmic bodies", "rods & rings", ] class _Split(Enum): TRAIN = "train" VAL = "val" SSL = "ssl" def get_csv_fpath(split): """ Path to data relative to root """ if split == _Split.TRAIN.value.upper() or split == _Split.TRAIN or split == "TRAIN": return "whole_images_512_train.csv" elif split == _Split.VAL.value.upper() or split == _Split.VAL or split == "VAL": return "whole_images_512_test.csv" class _WildCard(Enum): NONE = "none" SEPARATECHANNELS = "separate_channels" # each channel from each image is treated as an independent sample, overrides chosen channel configuration class _Mode(Enum): """ Targets: - ALL: tuple, (one hot encoding of multilabel protein localization, categorical encoding of cell type) - PROTEIN_LOCALIZATION: one hot encoding of multilabel protein localization - CELL_TYPE: categorical encoding of cell type """ ALL = "all" PROTEIN_LOCALIZATION = "protein_localization" CELL_TYPE = "cell_type" @property def nb_labels(self): if self == _Mode.CELL_TYPE: return len(CELL_TYPE) elif self == _Mode.PROTEIN_LOCALIZATION: return len(PROTEIN_LOCALIZATION) else: return None def _list_images_from_csv(img_path, csv_path): L = [] with open(csv_path) as filename: reader = csv.DictReader(filename) for row in reader: L.append(os.path.join(img_path, row["ID"] + ".png")) return L def _load_file_names_and_labels_ssl( root: str, ) -> Tuple[List[str], List[Any]]: curr_img_path = os.path.join(root, "normalized_data") csv_train_ssl = os.path.join(root, "whole_images_names.csv") image_paths = _list_images_from_csv(curr_img_path, csv_train_ssl) labels = [i for i in range(len(image_paths))] return image_paths, labels def _load_file_names_and_labels( root: str, split: _Split, mode: _Mode, ) -> Tuple[List[str], List[Any], np.ndarray]: data_path = os.path.join(root, "512_whole_images") csv_fpath = os.path.join(root, get_csv_fpath(split)) image_paths = [] labels = [] with open(csv_fpath) as filename: reader = csv.DictReader(filename) for row in reader: add_sample = True if mode != _Mode.PROTEIN_LOCALIZATION.value.upper(): # categorical if row["cell_type"] in CELL_TYPE: cell_type = CELL_TYPE.index(row["cell_type"]) else: cell_type = np.nan if mode != _Mode.CELL_TYPE.value.upper(): # one hot encoding prot_loc = np.zeros(len(PROTEIN_LOCALIZATION), dtype=np.int_) for k in range(len(PROTEIN_LOCALIZATION)): if row[PROTEIN_LOCALIZATION[k]] == "True": prot_loc[k] = 1 if prot_loc.max() < 0.5: add_sample = False if add_sample: if mode == _Mode.PROTEIN_LOCALIZATION.value.upper(): labels.append(prot_loc) elif mode == _Mode.CELL_TYPE.value.upper(): labels.append(cell_type) else: labels.append({"prot_loc": prot_loc, "cell_type": cell_type}) candidate_path = os.path.join(data_path, row["file"].split("/")[-1]) if os.path.exists(candidate_path): image_paths.append(candidate_path) else: candidate_path = os.path.join( data_path, row["file"].split("/")[-1].split(".")[0] + ".tiff" ) # _blue.png") # some images on the normalized_data folder have a _blue suffix on their names if os.path.exists(candidate_path): image_paths.append(candidate_path) else: raise FileNotFoundError(f"File {candidate_path} not found.") return image_paths, labels class HPAFoV(ExtendedVisionDataset): Split = Union[_Split] Mode = Union[_Mode] WildCard = Union[_WildCard] def __init__( self, *, split: "HPAFoV.Split" = _Split.TRAIN, mode: "HPAFoV.Mode" = _Mode.ALL, wildcard: "HPAFoV.WildCard" = _WildCard.NONE, root: str, transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, image_decoder_type: DecoderType = DecoderType.ChannelSelectDecoder, image_decoder_params: Dict[str, Any] = {}, **kwargs: Any, ) -> None: super().__init__( root, transforms, transform, target_transform, image_decoder_type=image_decoder_type, image_decoder_params={ "select_channel": True if wildcard == _WildCard.SEPARATECHANNELS or wildcard == "SEPARATE_CHANNELS" else False }, **kwargs, ) self.mode = mode self.split = split self.root = root self.wildcard = wildcard self.channel_adaptive = True if split == _Split.SSL.value.upper() or split == _Split.SSL or split == "SSL": self._image_paths, self._labels = _load_file_names_and_labels_ssl(root) else: self._image_paths, self._labels = _load_file_names_and_labels(root, self.split, self.mode) self._channels = np.repeat(np.array([[0, 1, 2, 3]]), len(self._image_paths), axis=0).tolist() if self.wildcard == _WildCard.SEPARATECHANNELS.value.upper(): image_paths, labels, channels = self._image_paths, self._labels, self._channels channels = np.array(channels) # separate and stack the columns of the channels array C = channels.shape[1] channels = np.concatenate([channels[:, i] for i in range(C)]) self._channels = np.expand_dims(channels, 1).tolist() self.image_paths = image_paths * C self.labels = labels * C def get_image_relpath(self, index: int) -> str: return self._image_paths[index] def get_image_data(self, index: int) -> bytes: image_relpath = self.get_image_relpath(index) image_full_path = os.path.join(self.root, image_relpath) with open(image_full_path, mode="rb") as f: image_data = f.read() if self.channel_adaptive: channels = self._channels[index] return image_data + bytes(channels) + (len(channels)).to_bytes(1, byteorder="big") else: return image_data def get_target(self, index: int) -> Any: return self._labels[index] def get_targets(self) -> np.ndarray: return np.array(self._labels) def __len__(self) -> int: return len(self._image_paths) ================================================ FILE: dinov2/data/datasets/cell_dino/hpaone.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import csv from enum import Enum import logging import os from typing import Any, Callable, List, Optional, Tuple, Union import numpy as np from ..extended import ExtendedVisionDataset from ..decoders import DecoderType logger = logging.getLogger("dinov2") PROTEIN_LOCALIZATION = [ "actin filaments,focal adhesion sites", "aggresome", "centrosome,centriolar satellite", "cytosol", "endoplasmic reticulum", "golgi apparatus", "intermediate filaments", "microtubules", "mitochondria", "mitotic spindle", "no staining", "nuclear bodies", "nuclear membrane", "nuclear speckles", "nucleoli", "nucleoli fibrillar center", "nucleoplasm", "plasma membrane,cell junctions", "vesicles,peroxisomes,endosomes,lysosomes,lipid droplets,cytoplasmic bodies", ] # 19 CELL_TYPE = [ "A-431", # 0 "A549", "AF22", "ASC TERT1", "BJ", "CACO-2", "EFO-21", "HAP1", "HDLM-2", "HEK 293", # 9 "HEL", "HUVEC TERT2", "HaCaT", "HeLa", "Hep G2", "JURKAT", "K-562", "MCF7", "PC-3", "REH", "RH-30", # 20 "RPTEC TERT1", "RT4", "SH-SY5Y", "SK-MEL-30", "SiHa", "U-2 OS", "U-251 MG", "hTCEpi", # 28 ] # 29 cell types class _Split(Enum): VAL = "val" TRAIN = "train" ALL = "all" # images without labels, for encoder training class _Mode(Enum): PROTEIN_LOCALIZATION = "protein_localization" CELL_TYPE = "cell_type" @property def num_labels(self): if self == _Mode.CELL_TYPE.value.upper(): return len(CELL_TYPE) return len(PROTEIN_LOCALIZATION) def _simple_parse_csv(img_rootdir, csv_filepath: str): samples = [] with open(csv_filepath) as filename: template = csv.DictReader(filename) samples = [(os.path.join(img_rootdir, row["img_path"]), 0) for row in template] return samples def _parse_csv(img_rootdir, csv_labels_path: str): nb_protein_location = len(PROTEIN_LOCALIZATION) nb_cell_type = len(CELL_TYPE) samples = [] with open(csv_labels_path) as filename: reader = csv.DictReader(filename) for row in reader: protein_location = np.zeros(nb_protein_location, dtype=np.int_) for k in range(nb_protein_location): if row[PROTEIN_LOCALIZATION[k]] == "True": protein_location[k] = 1 cell_type = 0 for k in range(nb_cell_type): if row[CELL_TYPE[k]] == "True": cell_type = k samples.append( ( img_rootdir + "/" + row["file"].rsplit("/", 1)[1], protein_location, cell_type, ) ) return samples def _load_file_names_and_labels_ssl( root: str, ) -> Tuple[List[str], List[Any]]: curr_dir_train = os.path.join(root, "varied_size_masked_single_cells_HPA") csv_all_path = os.path.join(root, "varied_size_masked_single_cells_pretrain_20240507.csv") samples = _simple_parse_csv(curr_dir_train, csv_all_path) image_paths, fake_labels = zip(*samples) lab = list(fake_labels) return image_paths, lab def _load_file_names_and_labels_train_or_test( root: str, split: _Split, mode: _Mode, ) -> Tuple[List[str], List[Any]]: if split == _Split.TRAIN.value.upper() or split == _Split.TRAIN: csv_labels_path = os.path.join(root, "fixed_size_masked_single_cells_pretrain_20240507.csv") elif split == _Split.VAL.value.upper() or split == _Split.VAL: csv_labels_path = os.path.join(root, "fixed_size_masked_single_cells_evaluation_20240507.csv") else: print("wrong split name") curr_dir_val = os.path.join(root, "fixed_size_masked_single_cells_HPA") samples = _parse_csv(curr_dir_val, csv_labels_path) image_paths, protein_location, cell_type = zip(*samples) if mode == _Mode.PROTEIN_LOCALIZATION.value.upper(): lab = protein_location elif mode == _Mode.CELL_TYPE.value.upper(): lab = cell_type else: lab = protein_location, cell_type image_paths = list(image_paths) return image_paths, lab class HPAone(ExtendedVisionDataset): Split = Union[_Split] Mode = Union[_Mode] def __init__( self, *, split: "HPAone.Split" = _Split.ALL, mode: "HPAone.Mode" = None, root: str, transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, image_decoder_type: DecoderType = DecoderType.XChannelsDecoder, **kwargs: Any, ) -> None: super().__init__( root, transforms, transform, target_transform, image_decoder_type=image_decoder_type, **kwargs, ) self.mode = mode self.split = split self.root = root if ( split in {_Split.TRAIN.value.upper(), _Split.VAL.value.upper()} or split == _Split.TRAIN or split == _Split.VAL ): ( self._image_paths, self._labels, ) = _load_file_names_and_labels_train_or_test(root, split, mode) elif split == _Split.ALL.value.upper() or split == _Split.ALL: self._image_paths, self._labels = _load_file_names_and_labels_ssl(root) else: logger.info(f"unknown split: {split}, {_Split.ALL.value.upper()}") def get_image_relpath(self, index: int) -> str: return self._image_paths[index] def get_image_data(self, index: int) -> bytes: image_relpath = self.get_image_relpath(index) image_full_path = os.path.join(self.root, image_relpath) with open(image_full_path, mode="rb") as f: image_data = f.read() return image_data def get_target(self, index: int) -> Any: return self._labels[index] def get_targets(self) -> np.ndarray: return np.array(self._labels) def __len__(self) -> int: return len(self._image_paths) ================================================ FILE: dinov2/data/datasets/decoders.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from io import BytesIO from typing import Any, Type from PIL import Image import numpy as np import torch from enum import Enum try: import tifffile except ImportError: print("Could not import `tifffile`, TIFFImageDataDecoder will be disabled") class Decoder: def decode(self) -> Any: raise NotImplementedError class DecoderType(Enum): ImageDataDecoder = "ImageDataDecoder" XChannelsDecoder = "XChannelsDecoder" XChannelsTIFFDecoder = "XChannelsTIFFDecoder" ChannelSelectDecoder = "ChannelSelectDecoder" def get_class(self) -> Type[Decoder]: # noqa: C901 if self == DecoderType.ImageDataDecoder: return ImageDataDecoder if self == DecoderType.XChannelsDecoder: return XChannelsDecoder if self == DecoderType.XChannelsTIFFDecoder: return XChannelsTIFFDecoder if self == DecoderType.ChannelSelectDecoder: return ChannelSelectDecoder class ImageDataDecoder(Decoder): def __init__(self, image_data: bytes) -> None: self._image_data = image_data def decode(self) -> Image: f = BytesIO(self._image_data) return Image.open(f).convert(mode="RGB") class TargetDecoder(Decoder): def __init__(self, target: Any): self._target = target def decode(self) -> Any: return self._target class XChannelsDecoder(Decoder): def __init__(self, image_data: bytes) -> None: self._image_data = image_data def decode(self): im = np.asarray(Image.open(BytesIO(self._image_data))) if len(im.shape) == 2: im = np.reshape(im, (im.shape[0], im.shape[0], -1), order="F") return torch.Tensor(im).permute(2, 0, 1) class XChannelsTIFFDecoder(Decoder): def __init__(self, image_data: bytes, num_channels: int = 3) -> None: self._image_data = image_data self._num_channels = num_channels def decode(self): numpy_array = tifffile.imread(BytesIO(self._image_data)) numpy_array = np.reshape(numpy_array, (numpy_array.shape[0], -1, self._num_channels), order="F") return torch.Tensor(numpy_array).permute(2, 0, 1) class ChannelSelectDecoder(Decoder): def __init__(self, image_data: bytes, select_channel: bool = False) -> None: self.select_channel = select_channel if select_channel: self._image_data = image_data[:-1] self._channel = image_data[-1] else: self._image_data = image_data def decode(self): im = np.asarray(Image.open(BytesIO(self._image_data))) if self.select_channel: return torch.Tensor(im).permute(2, 0, 1)[[self._channel]] return torch.Tensor(im).permute(2, 0, 1) ================================================ FILE: dinov2/data/datasets/extended.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from typing import Any, Tuple from torchvision.datasets import VisionDataset from .decoders import DecoderType, TargetDecoder class ExtendedVisionDataset(VisionDataset): def __init__(self, *args, **kwargs) -> None: image_decoder_type = kwargs.pop("image_decoder_type", DecoderType.ImageDataDecoder) self._decoder_params = {} self._image_decoder_class = image_decoder_type.get_class() if "image_decoder_params" in kwargs: self._decoder_params = kwargs.pop("image_decoder_params") super().__init__(*args, **kwargs) # type: ignore def get_image_data(self, index: int) -> bytes: raise NotImplementedError def get_target(self, index: int) -> Any: raise NotImplementedError def __getitem__(self, index: int) -> Tuple[Any, Any]: try: image_data = self.get_image_data(index) image = self._image_decoder_class(image_data, **self._decoder_params).decode() except Exception as e: raise RuntimeError(f"can not read image for sample {index}") from e target = self.get_target(index) target = TargetDecoder(target).decode() if self.transforms is not None: image, target = self.transforms(image, target) return image, target def __len__(self) -> int: raise NotImplementedError ================================================ FILE: dinov2/data/datasets/image_net.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import csv from enum import Enum import logging import os from typing import Callable, List, Optional, Tuple, Union import numpy as np from .extended import ExtendedVisionDataset logger = logging.getLogger("dinov2") _Target = int class _Split(Enum): TRAIN = "train" VAL = "val" TEST = "test" # NOTE: torchvision does not support the test split @property def length(self) -> int: split_lengths = { _Split.TRAIN: 1_281_167, _Split.VAL: 50_000, _Split.TEST: 100_000, } return split_lengths[self] def get_dirname(self, class_id: Optional[str] = None) -> str: return self.value if class_id is None else os.path.join(self.value, class_id) def get_image_relpath(self, actual_index: int, class_id: Optional[str] = None) -> str: dirname = self.get_dirname(class_id) if self == _Split.TRAIN: basename = f"{class_id}_{actual_index}" else: # self in (_Split.VAL, _Split.TEST): basename = f"ILSVRC2012_{self.value}_{actual_index:08d}" return os.path.join(dirname, basename + ".JPEG") def parse_image_relpath(self, image_relpath: str) -> Tuple[str, int]: assert self != _Split.TEST dirname, filename = os.path.split(image_relpath) class_id = os.path.split(dirname)[-1] basename, _ = os.path.splitext(filename) actual_index = int(basename.split("_")[-1]) return class_id, actual_index class ImageNet(ExtendedVisionDataset): Target = Union[_Target] Split = Union[_Split] def __init__( self, *, split: "ImageNet.Split", root: str, extra: str, transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, ) -> None: super().__init__(root, transforms, transform, target_transform) self._extra_root = extra self._split = split self._entries = None self._class_ids = None self._class_names = None @property def split(self) -> "ImageNet.Split": return self._split def _get_extra_full_path(self, extra_path: str) -> str: return os.path.join(self._extra_root, extra_path) def _load_extra(self, extra_path: str) -> np.ndarray: extra_full_path = self._get_extra_full_path(extra_path) return np.load(extra_full_path, mmap_mode="r") def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None: extra_full_path = self._get_extra_full_path(extra_path) os.makedirs(self._extra_root, exist_ok=True) np.save(extra_full_path, extra_array) @property def _entries_path(self) -> str: return f"entries-{self._split.value.upper()}.npy" @property def _class_ids_path(self) -> str: return f"class-ids-{self._split.value.upper()}.npy" @property def _class_names_path(self) -> str: return f"class-names-{self._split.value.upper()}.npy" def _get_entries(self) -> np.ndarray: if self._entries is None: self._entries = self._load_extra(self._entries_path) assert self._entries is not None return self._entries def _get_class_ids(self) -> np.ndarray: if self._split == _Split.TEST: assert False, "Class IDs are not available in TEST split" if self._class_ids is None: self._class_ids = self._load_extra(self._class_ids_path) assert self._class_ids is not None return self._class_ids def _get_class_names(self) -> np.ndarray: if self._split == _Split.TEST: assert False, "Class names are not available in TEST split" if self._class_names is None: self._class_names = self._load_extra(self._class_names_path) assert self._class_names is not None return self._class_names def find_class_id(self, class_index: int) -> str: class_ids = self._get_class_ids() return str(class_ids[class_index]) def find_class_name(self, class_index: int) -> str: class_names = self._get_class_names() return str(class_names[class_index]) def get_image_data(self, index: int) -> bytes: entries = self._get_entries() actual_index = entries[index]["actual_index"] class_id = self.get_class_id(index) image_relpath = self.split.get_image_relpath(actual_index, class_id) image_full_path = os.path.join(self.root, image_relpath) with open(image_full_path, mode="rb") as f: image_data = f.read() return image_data def get_target(self, index: int) -> Optional[Target]: entries = self._get_entries() class_index = entries[index]["class_index"] return None if self.split == _Split.TEST else int(class_index) def get_targets(self) -> Optional[np.ndarray]: entries = self._get_entries() return None if self.split == _Split.TEST else entries["class_index"] def get_class_id(self, index: int) -> Optional[str]: entries = self._get_entries() class_id = entries[index]["class_id"] return None if self.split == _Split.TEST else str(class_id) def get_class_name(self, index: int) -> Optional[str]: entries = self._get_entries() class_name = entries[index]["class_name"] return None if self.split == _Split.TEST else str(class_name) def __len__(self) -> int: entries = self._get_entries() assert len(entries) == self.split.length return len(entries) def _load_labels(self, labels_path: str) -> List[Tuple[str, str]]: labels_full_path = os.path.join(self.root, labels_path) labels = [] try: with open(labels_full_path, "r") as f: reader = csv.reader(f) for row in reader: class_id, class_name = row labels.append((class_id, class_name)) except OSError as e: raise RuntimeError(f'can not read labels file "{labels_full_path}"') from e return labels def _dump_entries(self) -> None: split = self.split if split == ImageNet.Split.TEST: dataset = None sample_count = split.length max_class_id_length, max_class_name_length = 0, 0 else: labels_path = "labels.txt" logger.info(f'loading labels from "{labels_path}"') labels = self._load_labels(labels_path) # NOTE: Using torchvision ImageFolder for consistency from torchvision.datasets import ImageFolder dataset_root = os.path.join(self.root, split.get_dirname()) dataset = ImageFolder(dataset_root) sample_count = len(dataset) max_class_id_length, max_class_name_length = -1, -1 for sample in dataset.samples: _, class_index = sample class_id, class_name = labels[class_index] max_class_id_length = max(len(class_id), max_class_id_length) max_class_name_length = max(len(class_name), max_class_name_length) dtype = np.dtype( [ ("actual_index", " old_percent: logger.info(f"creating entries: {percent}%") old_percent = percent actual_index = index + 1 class_index = np.uint32(-1) class_id, class_name = "", "" entries_array[index] = (actual_index, class_index, class_id, class_name) else: class_names = {class_id: class_name for class_id, class_name in labels} assert dataset old_percent = -1 for index in range(sample_count): percent = 100 * (index + 1) // sample_count if percent > old_percent: logger.info(f"creating entries: {percent}%") old_percent = percent image_full_path, class_index = dataset.samples[index] image_relpath = os.path.relpath(image_full_path, self.root) class_id, actual_index = split.parse_image_relpath(image_relpath) class_name = class_names[class_id] entries_array[index] = (actual_index, class_index, class_id, class_name) logger.info(f'saving entries to "{self._entries_path}"') self._save_extra(entries_array, self._entries_path) def _dump_class_ids_and_names(self) -> None: split = self.split if split == ImageNet.Split.TEST: return entries_array = self._load_extra(self._entries_path) max_class_id_length, max_class_name_length, max_class_index = -1, -1, -1 for entry in entries_array: class_index, class_id, class_name = ( entry["class_index"], entry["class_id"], entry["class_name"], ) max_class_index = max(int(class_index), max_class_index) max_class_id_length = max(len(str(class_id)), max_class_id_length) max_class_name_length = max(len(str(class_name)), max_class_name_length) class_count = max_class_index + 1 class_ids_array = np.empty(class_count, dtype=f"U{max_class_id_length}") class_names_array = np.empty(class_count, dtype=f"U{max_class_name_length}") for entry in entries_array: class_index, class_id, class_name = ( entry["class_index"], entry["class_id"], entry["class_name"], ) class_ids_array[class_index] = class_id class_names_array[class_index] = class_name logger.info(f'saving class IDs to "{self._class_ids_path}"') self._save_extra(class_ids_array, self._class_ids_path) logger.info(f'saving class names to "{self._class_names_path}"') self._save_extra(class_names_array, self._class_names_path) def dump_extra(self) -> None: self._dump_entries() self._dump_class_ids_and_names() ================================================ FILE: dinov2/data/datasets/image_net_22k.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from dataclasses import dataclass from enum import Enum from functools import lru_cache from gzip import GzipFile from io import BytesIO from mmap import ACCESS_READ, mmap import os from typing import Any, Callable, List, Optional, Set, Tuple import warnings import numpy as np from .extended import ExtendedVisionDataset _Labels = int _DEFAULT_MMAP_CACHE_SIZE = 16 # Warning: This can exhaust file descriptors @dataclass class _ClassEntry: block_offset: int maybe_filename: Optional[str] = None @dataclass class _Entry: class_index: int # noqa: E701 start_offset: int end_offset: int filename: str class _Split(Enum): TRAIN = "train" VAL = "val" @property def length(self) -> int: return { _Split.TRAIN: 11_797_647, _Split.VAL: 561_050, }[self] def entries_path(self): return f"imagenet21kp_{self.value}.txt" def _get_tarball_path(class_id: str) -> str: return f"{class_id}.tar" def _make_mmap_tarball(tarballs_root: str, mmap_cache_size: int): @lru_cache(maxsize=mmap_cache_size) def _mmap_tarball(class_id: str) -> mmap: tarball_path = _get_tarball_path(class_id) tarball_full_path = os.path.join(tarballs_root, tarball_path) with open(tarball_full_path) as f: return mmap(fileno=f.fileno(), length=0, access=ACCESS_READ) return _mmap_tarball class ImageNet22k(ExtendedVisionDataset): _GZIPPED_INDICES: Set[int] = { 841_545, 1_304_131, 2_437_921, 2_672_079, 2_795_676, 2_969_786, 6_902_965, 6_903_550, 6_903_628, 7_432_557, 7_432_589, 7_813_809, 8_329_633, 10_296_990, 10_417_652, 10_492_265, 10_598_078, 10_782_398, 10_902_612, 11_203_736, 11_342_890, 11_397_596, 11_589_762, 11_705_103, 12_936_875, 13_289_782, } Labels = _Labels def __init__( self, *, root: str, extra: str, transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, mmap_cache_size: int = _DEFAULT_MMAP_CACHE_SIZE, ) -> None: super().__init__(root, transforms, transform, target_transform) self._extra_root = extra entries_path = self._get_entries_path(root) self._entries = self._load_extra(entries_path) class_ids_path = self._get_class_ids_path(root) self._class_ids = self._load_extra(class_ids_path) self._gzipped_indices = ImageNet22k._GZIPPED_INDICES self._mmap_tarball = _make_mmap_tarball(self._tarballs_root, mmap_cache_size) def _get_entries_path(self, root: Optional[str] = None) -> str: return "entries.npy" def _get_class_ids_path(self, root: Optional[str] = None) -> str: return "class-ids.npy" def _find_class_ids(self, path: str) -> List[str]: class_ids = [] with os.scandir(path) as entries: for entry in entries: root, ext = os.path.splitext(entry.name) if ext != ".tar": continue class_ids.append(root) return sorted(class_ids) def _load_entries_class_ids(self, root: Optional[str] = None) -> Tuple[List[_Entry], List[str]]: root = self.get_root(root) entries: List[_Entry] = [] class_ids = self._find_class_ids(root) for class_index, class_id in enumerate(class_ids): path = os.path.join(root, "blocks", f"{class_id}.log") class_entries = [] try: with open(path) as f: for line in f: line = line.rstrip() block, filename = line.split(":") block_offset = int(block[6:]) filename = filename[1:] maybe_filename = None if filename != "** Block of NULs **": maybe_filename = filename _, ext = os.path.splitext(filename) # assert ext == ".JPEG" class_entry = _ClassEntry(block_offset, maybe_filename) class_entries.append(class_entry) except OSError as e: raise RuntimeError(f'can not read blocks file "{path}"') from e assert class_entries[-1].maybe_filename is None for class_entry1, class_entry2 in zip(class_entries, class_entries[1:]): assert class_entry1.block_offset <= class_entry2.block_offset start_offset = 512 * class_entry1.block_offset end_offset = 512 * class_entry2.block_offset assert class_entry1.maybe_filename is not None filename = class_entry1.maybe_filename entry = _Entry(class_index, start_offset, end_offset, filename) # Skip invalid image files (PIL throws UnidentifiedImageError) if filename == "n06470073_47249.JPEG": continue entries.append(entry) return entries, class_ids def _load_extra(self, extra_path: str) -> np.ndarray: extra_root = self._extra_root extra_full_path = os.path.join(extra_root, extra_path) return np.load(extra_full_path, mmap_mode="r") def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None: extra_root = self._extra_root extra_full_path = os.path.join(extra_root, extra_path) os.makedirs(extra_root, exist_ok=True) np.save(extra_full_path, extra_array) @property def _tarballs_root(self) -> str: return self.root def find_class_id(self, class_index: int) -> str: return str(self._class_ids[class_index]) def get_image_data(self, index: int) -> bytes: entry = self._entries[index] class_id = entry["class_id"] class_mmap = self._mmap_tarball(class_id) start_offset, end_offset = entry["start_offset"], entry["end_offset"] try: mapped_data = class_mmap[start_offset:end_offset] data = mapped_data[512:] # Skip entry header block if len(data) >= 2 and tuple(data[:2]) == (0x1F, 0x8B): assert index in self._gzipped_indices, f"unexpected gzip header for sample {index}" with GzipFile(fileobj=BytesIO(data)) as g: data = g.read() except Exception as e: raise RuntimeError(f"can not retrieve image data for sample {index} " f'from "{class_id}" tarball') from e return data def get_target(self, index: int) -> Any: return int(self._entries[index]["class_index"]) def get_targets(self) -> np.ndarray: return self._entries["class_index"] def get_class_id(self, index: int) -> str: return str(self._entries[index]["class_id"]) def get_class_ids(self) -> np.ndarray: return self._entries["class_id"] def __getitem__(self, index: int) -> Tuple[Any, Any]: with warnings.catch_warnings(): warnings.simplefilter("ignore") return super().__getitem__(index) def __len__(self) -> int: return len(self._entries) def _dump_entries(self, *args, **kwargs) -> None: entries, class_ids = self._load_entries_class_ids(*args, **kwargs) max_class_id_length, max_filename_length, max_class_index = -1, -1, -1 for entry in entries: class_id = class_ids[entry.class_index] max_class_index = max(entry.class_index, max_class_index) max_class_id_length = max(len(class_id), max_class_id_length) max_filename_length = max(len(entry.filename), max_filename_length) dtype = np.dtype( [ ("class_index", " None: entries_path = self._get_entries_path(*args, **kwargs) entries_array = self._load_extra(entries_path) max_class_id_length, max_class_index = -1, -1 for entry in entries_array: class_index, class_id = entry["class_index"], entry["class_id"] max_class_index = max(int(class_index), max_class_index) max_class_id_length = max(len(str(class_id)), max_class_id_length) class_ids_array = np.empty(max_class_index + 1, dtype=f"U{max_class_id_length}") for entry in entries_array: class_index, class_id = entry["class_index"], entry["class_id"] class_ids_array[class_index] = class_id class_ids_path = self._get_class_ids_path(*args, **kwargs) self._save_extra(class_ids_array, class_ids_path) def _dump_extra(self, *args, **kwargs) -> None: self._dump_entries(*args, *kwargs) self._dump_class_ids(*args, *kwargs) def dump_extra(self, root: Optional[str] = None) -> None: return self._dump_extra(root) ================================================ FILE: dinov2/data/loaders.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging from enum import Enum from typing import Any, Callable, List, Optional, TypeVar import torch from torch.utils.data import Sampler from .datasets import ImageNet, ImageNet22k, HPAone, HPAFoV, CHAMMI_CP, CHAMMI_HPA, CHAMMI_WTC from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler logger = logging.getLogger("dinov2") class SamplerType(Enum): DISTRIBUTED = 0 EPOCH = 1 INFINITE = 2 SHARDED_INFINITE = 3 SHARDED_INFINITE_NEW = 4 def _make_bool_str(b: bool) -> str: return "yes" if b else "no" def _make_sample_transform(image_transform: Optional[Callable] = None, target_transform: Optional[Callable] = None): def transform(sample): image, target = sample if image_transform is not None: image = image_transform(image) if target_transform is not None: target = target_transform(target) return image, target return transform def _parse_dataset_str(dataset_str: str): tokens = dataset_str.split(":") name = tokens[0] kwargs = {} for token in tokens[1:]: key, value = token.split("=") assert key in ("root", "extra", "split", "mode", "wildcard") kwargs[key] = value if name == "ImageNet": class_ = ImageNet if "split" in kwargs: kwargs["split"] = ImageNet.Split[kwargs["split"]] elif name == "ImageNet22k": class_ = ImageNet22k elif name == "HPAone": class_ = HPAone elif name == "HPAFoV": class_ = HPAFoV elif name == "CHAMMI_CP": class_ = CHAMMI_CP elif name == "CHAMMI_WTC": class_ = CHAMMI_WTC elif name == "CHAMMI_HPA": class_ = CHAMMI_HPA else: raise ValueError(f'Unsupported dataset "{name}"') return class_, kwargs def make_dataset( *, dataset_str: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, ): """ Creates a dataset with the specified parameters. Args: dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN). transform: A transform to apply to images. target_transform: A transform to apply to targets. Returns: The created dataset. """ logger.info(f'using dataset: "{dataset_str}"') class_, kwargs = _parse_dataset_str(dataset_str) dataset = class_(transform=transform, target_transform=target_transform, **kwargs) logger.info(f"# of dataset samples: {len(dataset):,d}") # Aggregated datasets do not expose (yet) these attributes, so add them. if not hasattr(dataset, "transform"): setattr(dataset, "transform", transform) if not hasattr(dataset, "target_transform"): setattr(dataset, "target_transform", target_transform) return dataset def _make_sampler( *, dataset, type: Optional[SamplerType] = None, shuffle: bool = False, seed: int = 0, size: int = -1, advance: int = 0, ) -> Optional[Sampler]: sample_count = len(dataset) if type == SamplerType.INFINITE: logger.info("sampler: infinite") if size > 0: raise ValueError("sampler size > 0 is invalid") return InfiniteSampler( sample_count=sample_count, shuffle=shuffle, seed=seed, advance=advance, ) elif type in (SamplerType.SHARDED_INFINITE, SamplerType.SHARDED_INFINITE_NEW): logger.info("sampler: sharded infinite") if size > 0: raise ValueError("sampler size > 0 is invalid") # TODO: Remove support for old shuffling use_new_shuffle_tensor_slice = type == SamplerType.SHARDED_INFINITE_NEW return ShardedInfiniteSampler( sample_count=sample_count, shuffle=shuffle, seed=seed, advance=advance, use_new_shuffle_tensor_slice=use_new_shuffle_tensor_slice, ) elif type == SamplerType.EPOCH: logger.info("sampler: epoch") if advance > 0: raise NotImplementedError("sampler advance > 0 is not supported") size = size if size > 0 else sample_count logger.info(f"# of samples / epoch: {size:,d}") return EpochSampler( size=size, sample_count=sample_count, shuffle=shuffle, seed=seed, ) elif type == SamplerType.DISTRIBUTED: logger.info("sampler: distributed") if size > 0: raise ValueError("sampler size > 0 is invalid") if advance > 0: raise ValueError("sampler advance > 0 is invalid") return torch.utils.data.DistributedSampler( dataset=dataset, shuffle=shuffle, seed=seed, drop_last=False, ) logger.info("sampler: none") return None T = TypeVar("T") def make_data_loader( *, dataset, batch_size: int, num_workers: int, shuffle: bool = True, seed: int = 0, sampler_type: Optional[SamplerType] = SamplerType.INFINITE, sampler_size: int = -1, sampler_advance: int = 0, drop_last: bool = True, persistent_workers: bool = False, collate_fn: Optional[Callable[[List[T]], Any]] = None, ): """ Creates a data loader with the specified parameters. Args: dataset: A dataset (third party, LaViDa or WebDataset). batch_size: The size of batches to generate. num_workers: The number of workers to use. shuffle: Whether to shuffle samples. seed: The random seed to use. sampler_type: Which sampler to use: EPOCH, INFINITE, SHARDED_INFINITE, SHARDED_INFINITE_NEW, DISTRIBUTED or None. sampler_size: The number of images per epoch (when applicable) or -1 for the entire dataset. sampler_advance: How many samples to skip (when applicable). drop_last: Whether the last non-full batch of data should be dropped. persistent_workers: maintain the workers Dataset instances alive after a dataset has been consumed once. collate_fn: Function that performs batch collation """ sampler = _make_sampler( dataset=dataset, type=sampler_type, shuffle=shuffle, seed=seed, size=sampler_size, advance=sampler_advance, ) logger.info("using PyTorch data loader") data_loader = torch.utils.data.DataLoader( dataset, sampler=sampler, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=drop_last, persistent_workers=persistent_workers, collate_fn=collate_fn, ) try: logger.info(f"# of batches: {len(data_loader):,d}") except TypeError: # data loader has no length logger.info("infinite data loader") return data_loader ================================================ FILE: dinov2/data/masking.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import random import math import numpy as np class MaskingGenerator: def __init__( self, input_size, num_masking_patches=None, min_num_patches=4, max_num_patches=None, min_aspect=0.3, max_aspect=None, ): if not isinstance(input_size, tuple): input_size = (input_size,) * 2 self.height, self.width = input_size self.num_patches = self.height * self.width self.num_masking_patches = num_masking_patches self.min_num_patches = min_num_patches self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches max_aspect = max_aspect or 1 / min_aspect self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect)) def __repr__(self): repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % ( self.height, self.width, self.min_num_patches, self.max_num_patches, self.num_masking_patches, self.log_aspect_ratio[0], self.log_aspect_ratio[1], ) return repr_str def get_shape(self): return self.height, self.width def _mask(self, mask, max_mask_patches): delta = 0 for _ in range(10): target_area = random.uniform(self.min_num_patches, max_mask_patches) aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio)) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if w < self.width and h < self.height: top = random.randint(0, self.height - h) left = random.randint(0, self.width - w) num_masked = mask[top : top + h, left : left + w].sum() # Overlap if 0 < h * w - num_masked <= max_mask_patches: for i in range(top, top + h): for j in range(left, left + w): if mask[i, j] == 0: mask[i, j] = 1 delta += 1 if delta > 0: break return delta def __call__(self, num_masking_patches=0): mask = np.zeros(shape=self.get_shape(), dtype=bool) mask_count = 0 while mask_count < num_masking_patches: max_mask_patches = num_masking_patches - mask_count max_mask_patches = min(max_mask_patches, self.max_num_patches) delta = self._mask(mask, max_mask_patches) if delta == 0: break else: mask_count += delta return mask ================================================ FILE: dinov2/data/samplers.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import itertools from typing import Any, Optional import warnings import numpy as np import torch from torch.utils.data.sampler import Sampler import dinov2.distributed as distributed class EpochSampler(Sampler): def __init__( self, *, size: int, sample_count: int, shuffle: bool = False, seed: int = 0, start: Optional[int] = None, step: Optional[int] = None, ): self._size = size self._sample_count = sample_count self._shuffle = shuffle self._seed = seed self._start = distributed.get_global_rank() if start is None else start self._step = distributed.get_global_size() if step is None else step self._epoch = 0 def __iter__(self): count = (self._size + self._sample_count - 1) // self._sample_count tiled_indices = np.tile(np.arange(self._sample_count), count) if self._shuffle: seed = self._seed * self._epoch if self._seed != 0 else self._epoch rng = np.random.default_rng(seed) iterable = rng.choice(tiled_indices, self._size, replace=False) else: iterable = tiled_indices[: self._size] yield from itertools.islice(iterable, self._start, None, self._step) def __len__(self): return (self._size - self._start + self._step - 1) // self._step def set_epoch(self, epoch): self._epoch = epoch def _get_numpy_dtype(size: int) -> Any: return np.int32 if size <= 2**31 else np.int64 def _get_torch_dtype(size: int) -> Any: return torch.int32 if size <= 2**31 else torch.int64 def _generate_randperm_indices(*, size: int, generator: torch.Generator): """Generate the indices of a random permutation.""" dtype = _get_torch_dtype(size) # This is actually matching PyTorch's CPU implementation, see: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#L900-L921 perm = torch.arange(size, dtype=dtype) for i in range(size): j = torch.randint(i, size, size=(1,), generator=generator).item() # Always swap even if no-op value = perm[j].item() perm[j] = perm[i].item() perm[i] = value yield value class InfiniteSampler(Sampler): def __init__( self, *, sample_count: int, shuffle: bool = False, seed: int = 0, start: Optional[int] = None, step: Optional[int] = None, advance: int = 0, ): self._sample_count = sample_count self._seed = seed self._shuffle = shuffle self._start = distributed.get_global_rank() if start is None else start self._step = distributed.get_global_size() if step is None else step self._advance = advance def __iter__(self): if self._shuffle: iterator = self._shuffled_iterator() else: iterator = self._iterator() yield from itertools.islice(iterator, self._advance, None) def _iterator(self): assert not self._shuffle while True: iterable = range(self._sample_count) yield from itertools.islice(iterable, self._start, None, self._step) def _shuffled_iterator(self): assert self._shuffle # Instantiate a generator here (rather than in the ctor) to keep the class # picklable (requirement of mp.spawn) generator = torch.Generator().manual_seed(self._seed) while True: iterable = _generate_randperm_indices(size=self._sample_count, generator=generator) yield from itertools.islice(iterable, self._start, None, self._step) # The following function is somewhat equivalent to _new_shuffle_tensor_slice below, # but avoids a full in-place random permutation generation. def _shuffle_tensor_slice( *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator ) -> np.ndarray: stop = len(tensor) count = stop // step drop_count = stop - step * count if drop_count: warnings.warn(f"# of dropped samples: {drop_count}") dtype = _get_numpy_dtype(stop) result = np.empty(count, dtype=dtype) for i in range(count): j = torch.randint(0, i + 1, size=(1,), generator=generator).item() if i > 0 else 0 result[i] = result[j] result[j] = tensor[start + i * step].item() return result def _new_shuffle_tensor_slice( *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator ) -> np.ndarray: stop = len(tensor) count = stop // step dtype = torch.int64 # Needed for using randperm result as indices count = stop // step drop_count = stop - step * count if drop_count: warnings.warn(f"# of dropped samples: {drop_count}") indices = torch.randperm(count, dtype=dtype, generator=generator) return tensor[start::step][indices].numpy() def _make_seed(seed: int, start: int, iter_count: int) -> int: # NOTE: Tried a few variants (including iter_count << 32), this one worked best. return seed + start + (iter_count << 24) class ShardedInfiniteSampler(Sampler): def __init__( self, *, sample_count: int, shuffle: bool = False, seed: int = 0, start: Optional[int] = None, step: Optional[int] = None, advance: int = 0, use_new_shuffle_tensor_slice: bool = False, ): self._sample_count = sample_count self._seed = seed self._shuffle = shuffle self._start = distributed.get_global_rank() if start is None else start self._step = distributed.get_global_size() if step is None else step self._advance = advance self._iter_count = 0 self._shuffle_tensor_slice_fn = ( _new_shuffle_tensor_slice if use_new_shuffle_tensor_slice else _shuffle_tensor_slice ) def __iter__(self): iter_count = self._advance // self._sample_count if iter_count > 0: self._advance -= iter_count * self._sample_count self._iter_count += iter_count if self._shuffle: iterator = self._shuffled_iterator() else: iterator = self._iterator() yield from itertools.islice(iterator, self._advance, None) def _iterator(self): assert not self._shuffle while True: iterable = range(self._sample_count) yield from itertools.islice(iterable, self._start, None, self._step) def _shuffled_iterator(self): assert self._shuffle # Instantiate a generator here (rather than in the ctor) to be keep the class # picklable (requirement of mp.spawn) generator = torch.Generator() # Always shuffle everything first generator.manual_seed(self._seed) dtype = _get_torch_dtype(self._sample_count) perm = torch.randperm(self._sample_count, dtype=dtype, generator=generator) while True: # Re-seed on each iteration to allow skipping whole permutations seed = _make_seed(self._seed, self._start, self._iter_count) generator.manual_seed(seed) iterable = self._shuffle_tensor_slice_fn( tensor=perm, start=self._start, step=self._step, generator=generator ) yield from iterable self._iter_count += 1 ================================================ FILE: dinov2/data/transforms.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from typing import Sequence import torch from torchvision import transforms class GaussianBlur(transforms.RandomApply): """ Apply Gaussian Blur to the PIL image. """ def __init__(self, *, p: float = 0.5, radius_min: float = 0.1, radius_max: float = 2.0): # NOTE: torchvision is applying 1 - probability to return the original image keep_p = 1 - p transform = transforms.GaussianBlur(kernel_size=9, sigma=(radius_min, radius_max)) super().__init__(transforms=[transform], p=keep_p) class MaybeToTensor(transforms.ToTensor): """ Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor, or keep as is if already a tensor. """ def __call__(self, pic): """ Args: pic (PIL Image, numpy.ndarray or torch.tensor): Image to be converted to tensor. Returns: Tensor: Converted image. """ if isinstance(pic, torch.Tensor): return pic return super().__call__(pic) # Use timm's names IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) def make_normalize_transform( mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, std: Sequence[float] = IMAGENET_DEFAULT_STD, ) -> transforms.Normalize: return transforms.Normalize(mean=mean, std=std) # This roughly matches torchvision's preset for classification training: # https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L6-L44 def make_classification_train_transform( *, crop_size: int = 224, interpolation=transforms.InterpolationMode.BICUBIC, hflip_prob: float = 0.5, mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, std: Sequence[float] = IMAGENET_DEFAULT_STD, ): transforms_list = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)] if hflip_prob > 0.0: transforms_list.append(transforms.RandomHorizontalFlip(hflip_prob)) transforms_list.extend( [ MaybeToTensor(), make_normalize_transform(mean=mean, std=std), ] ) return transforms.Compose(transforms_list) # This matches (roughly) torchvision's preset for classification evaluation: # https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L47-L69 def make_classification_eval_transform( *, resize_size: int = 256, interpolation=transforms.InterpolationMode.BICUBIC, crop_size: int = 224, mean: Sequence[float] = IMAGENET_DEFAULT_MEAN, std: Sequence[float] = IMAGENET_DEFAULT_STD, ) -> transforms.Compose: transforms_list = [ transforms.Resize(resize_size, interpolation=interpolation), transforms.CenterCrop(crop_size), MaybeToTensor(), make_normalize_transform(mean=mean, std=std), ] return transforms.Compose(transforms_list) ================================================ FILE: dinov2/distributed/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import os import random import re import socket from typing import Dict, List import torch import torch.distributed as dist _LOCAL_RANK = -1 _LOCAL_WORLD_SIZE = -1 def is_enabled() -> bool: """ Returns: True if distributed training is enabled """ return dist.is_available() and dist.is_initialized() def get_global_size() -> int: """ Returns: The number of processes in the process group """ return dist.get_world_size() if is_enabled() else 1 def get_global_rank() -> int: """ Returns: The rank of the current process within the global process group. """ return dist.get_rank() if is_enabled() else 0 def get_local_rank() -> int: """ Returns: The rank of the current process within the local (per-machine) process group. """ if not is_enabled(): return 0 assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE return _LOCAL_RANK def get_local_size() -> int: """ Returns: The size of the per-machine process group, i.e. the number of processes per machine. """ if not is_enabled(): return 1 assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE return _LOCAL_WORLD_SIZE def is_main_process() -> bool: """ Returns: True if the current process is the main one. """ return get_global_rank() == 0 def _restrict_print_to_main_process() -> None: """ This function disables printing when not in the main process """ import builtins as __builtin__ builtin_print = __builtin__.print def print(*args, **kwargs): force = kwargs.pop("force", False) if is_main_process() or force: builtin_print(*args, **kwargs) __builtin__.print = print def _get_master_port(seed: int = 0) -> int: MIN_MASTER_PORT, MAX_MASTER_PORT = (20_000, 60_000) master_port_str = os.environ.get("MASTER_PORT") if master_port_str is None: rng = random.Random(seed) return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT) return int(master_port_str) def _get_available_port() -> int: with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: # A "" host address means INADDR_ANY i.e. binding to all interfaces. # Note this is not compatible with IPv6. s.bind(("", 0)) port = s.getsockname()[1] return port _TORCH_DISTRIBUTED_ENV_VARS = ( "MASTER_ADDR", "MASTER_PORT", "RANK", "WORLD_SIZE", "LOCAL_RANK", "LOCAL_WORLD_SIZE", ) def _collect_env_vars() -> Dict[str, str]: return {env_var: os.environ[env_var] for env_var in _TORCH_DISTRIBUTED_ENV_VARS if env_var in os.environ} def _is_slurm_job_process() -> bool: return "SLURM_JOB_ID" in os.environ def _parse_slurm_node_list(s: str) -> List[str]: nodes = [] # Extract "hostname", "hostname[1-2,3,4-5]," substrings p = re.compile(r"(([^\[]+)(?:\[([^\]]+)\])?),?") for m in p.finditer(s): prefix, suffixes = s[m.start(2) : m.end(2)], s[m.start(3) : m.end(3)] for suffix in suffixes.split(","): span = suffix.split("-") if len(span) == 1: nodes.append(prefix + suffix) else: width = len(span[0]) start, end = int(span[0]), int(span[1]) + 1 nodes.extend([prefix + f"{i:0{width}}" for i in range(start, end)]) return nodes def _check_env_variable(key: str, new_value: str): # Only check for difference with preset environment variables if key in os.environ and os.environ[key] != new_value: raise RuntimeError(f"Cannot export environment variables as {key} is already set") class _TorchDistributedEnvironment: def __init__(self): self.master_addr = "127.0.0.1" self.master_port = 0 self.rank = -1 self.world_size = -1 self.local_rank = -1 self.local_world_size = -1 if _is_slurm_job_process(): return self._set_from_slurm_env() env_vars = _collect_env_vars() if not env_vars: # Environment is not set pass elif len(env_vars) == len(_TORCH_DISTRIBUTED_ENV_VARS): # Environment is fully set return self._set_from_preset_env() else: # Environment is partially set collected_env_vars = ", ".join(env_vars.keys()) raise RuntimeError(f"Partially set environment: {collected_env_vars}") if torch.cuda.device_count() > 0: return self._set_from_local() raise RuntimeError("Can't initialize PyTorch distributed environment") # Slurm job created with sbatch, submitit, etc... def _set_from_slurm_env(self): # logger.info("Initialization from Slurm environment") job_id = int(os.environ["SLURM_JOB_ID"]) node_count = int(os.environ["SLURM_JOB_NUM_NODES"]) nodes = _parse_slurm_node_list(os.environ["SLURM_JOB_NODELIST"]) assert len(nodes) == node_count self.master_addr = nodes[0] self.master_port = _get_master_port(seed=job_id) self.rank = int(os.environ["SLURM_PROCID"]) self.world_size = int(os.environ["SLURM_NTASKS"]) assert self.rank < self.world_size self.local_rank = int(os.environ["SLURM_LOCALID"]) self.local_world_size = self.world_size // node_count assert self.local_rank < self.local_world_size # Single node job with preset environment (i.e. torchrun) def _set_from_preset_env(self): # logger.info("Initialization from preset environment") self.master_addr = os.environ["MASTER_ADDR"] self.master_port = os.environ["MASTER_PORT"] self.rank = int(os.environ["RANK"]) self.world_size = int(os.environ["WORLD_SIZE"]) assert self.rank < self.world_size self.local_rank = int(os.environ["LOCAL_RANK"]) self.local_world_size = int(os.environ["LOCAL_WORLD_SIZE"]) assert self.local_rank < self.local_world_size # Single node and GPU job (i.e. local script run) def _set_from_local(self): # logger.info("Initialization from local") self.master_addr = "127.0.0.1" self.master_port = _get_available_port() self.rank = 0 self.world_size = 1 self.local_rank = 0 self.local_world_size = 1 def export(self, *, overwrite: bool) -> "_TorchDistributedEnvironment": # See the "Environment variable initialization" section from # https://pytorch.org/docs/stable/distributed.html for the complete list of # environment variables required for the env:// initialization method. env_vars = { "MASTER_ADDR": self.master_addr, "MASTER_PORT": str(self.master_port), "RANK": str(self.rank), "WORLD_SIZE": str(self.world_size), "LOCAL_RANK": str(self.local_rank), "LOCAL_WORLD_SIZE": str(self.local_world_size), } if not overwrite: for k, v in env_vars.items(): _check_env_variable(k, v) os.environ.update(env_vars) return self def enable(*, set_cuda_current_device: bool = True, overwrite: bool = False, allow_nccl_timeout: bool = False): """Enable distributed mode Args: set_cuda_current_device: If True, call torch.cuda.set_device() to set the current PyTorch CUDA device to the one matching the local rank. overwrite: If True, overwrites already set variables. Else fails. """ global _LOCAL_RANK, _LOCAL_WORLD_SIZE if _LOCAL_RANK >= 0 or _LOCAL_WORLD_SIZE >= 0: raise RuntimeError("Distributed mode has already been enabled") torch_env = _TorchDistributedEnvironment() torch_env.export(overwrite=overwrite) if set_cuda_current_device: torch.cuda.set_device(torch_env.local_rank) if allow_nccl_timeout: # This allows to use torch distributed timeout in a NCCL backend key, value = "NCCL_ASYNC_ERROR_HANDLING", "1" if not overwrite: _check_env_variable(key, value) os.environ[key] = value dist.init_process_group(backend="nccl") dist.barrier() # Finalize setup _LOCAL_RANK = torch_env.local_rank _LOCAL_WORLD_SIZE = torch_env.local_world_size _restrict_print_to_main_process() ================================================ FILE: dinov2/eval/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. ================================================ FILE: dinov2/eval/cell_dino/knn.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import argparse from functools import partial import json import logging import os import sys from typing import List, Optional, Any import numpy as np import torch import torch.backends.cudnn as cudnn import pandas as pd from sklearn.metrics import f1_score import dinov2.distributed as distributed from dinov2.data import make_dataset, DatasetWithEnumeratedTargets, SamplerType, make_data_loader from dinov2.data.cell_dino.transforms import NormalizationType, make_classification_eval_cell_transform from dinov2.eval.metrics import build_metric, MetricType from dinov2.eval.setup import get_args_parser as get_setup_args_parser from dinov2.eval.setup import setup_and_build_model from dinov2.data import ResultsAccumulator from dinov2.eval.utils import ModelWithNormalize from dinov2.eval.cell_dino.utils import ( BagOfChannelsModelWithNormalize, extract_features_cell_dino, average_metrics, create_train_dataset_dict, get_num_classes, extract_features_for_dataset_dict, evaluate_with_accumulate, KnnModule, ) from dinov2.eval.knn import DictKeysModule from torch.utils.data import Subset as SubsetEx from torch.utils.data import ConcatDataset as ConcatDatasetEx logger = logging.getLogger("dinov2") def get_args_parser( description: Optional[str] = None, parents: Optional[List[argparse.ArgumentParser]] = None, add_help: bool = True, ): parents = parents or [] setup_args_parser = get_setup_args_parser(parents=parents, add_help=False) parents = [setup_args_parser] parser = argparse.ArgumentParser( description=description, parents=parents, add_help=add_help, ) parser.add_argument( "--train-dataset", dest="train_dataset_str", type=str, help="Training dataset", ) parser.add_argument( "--val-dataset", dest="val_dataset_str", type=str, help="Validation dataset", ) parser.add_argument( "--nb_knn", nargs="+", type=int, help="Number of NN to use. 20 is usually working the best.", ) parser.add_argument( "--temperature", type=float, help="Temperature used in the voting coefficient", ) parser.add_argument( "--gather-on-cpu", action="store_true", help="Whether to gather the train features on cpu, slower" "but useful to avoid OOM for large datasets (e.g. ImageNet22k).", ) parser.add_argument( "--batch-size", type=int, help="Batch size.", ) parser.add_argument( "--n-per-class-list", nargs="+", type=int, help="Number to take per class", ) parser.add_argument( "--n-tries", type=int, help="Number of tries", ) parser.add_argument( "--leave-one-out-dataset", type=str, help="Path with indexes to use the leave one out strategy for CHAMMI_CP task 3 and CHAMMI_HPA task 4", ) parser.add_argument( "--bag-of-channels", action="store_true", help='Whether to use the "bag of channels" channel adaptive strategy', ) parser.add_argument( "--crop-size", type=int, help="crop size for train and eval", ) parser.add_argument( "--resize-size", type=int, help="resize size for image just before crop. 0: no resize", ) parser.add_argument( "--metric-type", type=MetricType, choices=list(MetricType), help="Validation metric", ) parser.add_argument( "--avgpool", action="store_true", help="Whether to use average pooling of path tokens in addition to CLS tokens", ) parser.set_defaults( train_dataset_str="ImageNet:split=TRAIN", val_dataset_str="ImageNet:split=VAL", nb_knn=[1], temperature=0.07, batch_size=256, resize_size=0, ) return parser class SequentialWithKwargs(torch.nn.Sequential): def __init__(self, *args): super().__init__(*args) def forward(self, input, **kwargs): input = self[0](input, **kwargs) for module in self[1:]: input = module(input) return input def create_train_test_dataset_dict_leave_one_out( train_dataset, test_dataset, ) -> dict[int, dict[int, Any]]: """ This function implements a train dataset dictionary with the leave-one-out (LOO) method. Specifically, given a train dataset and test dataset, it creates a train dataset for each test dataset point, which is a combination of train+test dataset except for this specific data point. At the end, it contains len(test_dataset) key and value pairs. Format is {"nth-test-sample": dataset_without_test_sample} """ train_dataset_dict: dict[int, Any] = {} test_size = len(test_dataset) for test_sample_index in range(test_size): test_indices_bool = torch.ones(test_size, dtype=bool) test_indices_bool[test_sample_index] = False train_dataset_dict[test_sample_index] = ConcatDatasetEx( [train_dataset, SubsetEx(test_dataset, test_indices_bool.nonzero().flatten())] ) return train_dataset_dict def eval_knn_with_leave_one_out( model, leave_one_out_dataset, train_dataset, test_dataset, metric_type, nb_knn, temperature, batch_size, num_workers ): num_classes = get_num_classes(test_dataset) train_dataset_dict = create_train_dataset_dict(train_dataset) test_dataset_dict = create_train_dataset_dict(test_dataset) logger.info("Extracting features for train set...") train_data_dict = extract_features_for_dataset_dict( model, train_dataset_dict, batch_size, num_workers, gather_on_cpu=True ) test_data_dict = extract_features_for_dataset_dict( model, test_dataset_dict, batch_size, num_workers, gather_on_cpu=True ) train_features = train_data_dict[0]["train_features"] train_labels = train_data_dict[0]["train_labels"] test_features = test_data_dict[0]["train_features"] test_labels = test_data_dict[0]["train_labels"] metric_collection = build_metric(metric_type, num_classes=3) device = torch.cuda.current_device() partial_knn_module = partial(KnnModule, T=temperature, device=device, num_classes=num_classes) logger.info("Reading the leave-one-out label metadata.") leave_one_out_indices = {} metadata = pd.read_csv(leave_one_out_dataset) if "HPA" in leave_one_out_dataset: metadata = metadata[metadata["Task_three"]].reset_index() leave_one_out_label_type = "cell_type" else: metadata = metadata[metadata["Task_four"]].reset_index() leave_one_out_label_type = "Plate" leave_one_out_labels = metadata[leave_one_out_label_type].unique() for leave_one_out_label in leave_one_out_labels: leave_one_out_indices[leave_one_out_label] = torch.tensor( metadata[metadata[leave_one_out_label_type] == leave_one_out_label].index.values ) # ============ evaluation ... ============ logger.info("Start the k-NN classification.") eval_metrics_dict = {} postprocessors, metrics = {k: DictKeysModule([k]) for k in nb_knn}, { k: metric_collection.clone().to(device) for k in nb_knn } for metric_key in metrics.keys(): metrics[metric_key] = metrics[metric_key].to(device) accumulator_class = ResultsAccumulator accumulators = {k: accumulator_class() for k in postprocessors.keys()} all_preds = [] all_target = [] for loo_label, loo_indices in leave_one_out_indices.items(): logger.info(f"Evaluating on test sample {loo_label}") loo_for_training_indices = torch.ones(test_features.shape[0], dtype=bool) loo_for_training_indices[loo_indices] = False train_features_sample = torch.cat([train_features, test_features[loo_for_training_indices]]) train_labels_sample = torch.cat([train_labels, test_labels[loo_for_training_indices]]) logger.info(f"Train shape {train_features_sample.shape}, Test shape {test_features[loo_indices].shape}") logger.info( f"Train values {train_labels_sample.unique(return_counts=True)}, Test shape {test_labels[loo_indices].unique(return_counts=True)}" ) knn_module = partial_knn_module( train_features=train_features_sample, train_labels=train_labels_sample, nb_knn=nb_knn ) output = knn_module(test_features[loo_indices].to(device)) all_preds.append(output[1]) all_target.append(test_labels[loo_indices]) output[1] = output[1][:, 4:] transformed_test_labels = test_labels[loo_indices] - 4 for k, metric in metrics.items(): metric_inputs = postprocessors[k](output, transformed_test_labels.to(device)) metric.update(**metric_inputs) accumulators[k].update( preds=metric_inputs["preds"], target=metric_inputs["target"], index=loo_indices.to(device) ) all_preds = torch.cat(all_preds).cpu().detach().numpy() all_preds = np.argmax(all_preds, axis=1) all_target = torch.cat(all_target).cpu().detach().numpy() f1 = f1_score(all_target, all_preds, average="macro", labels=[4, 5, 6]) logger.info(f"Real f1 score: {f1}") eval_metrics = { k: metric.compute() for k, metric in metrics.items() } # next erased by the real f1 score computed above for k in nb_knn: if k not in eval_metrics_dict: eval_metrics_dict[k] = {} eval_metrics_dict[k] = {metric: f1 * 100.0 for metric, v in eval_metrics[k].items()} if len(train_data_dict) > 1: return {k: average_metrics(eval_metrics_dict[k]) for k in eval_metrics_dict.keys()} return {k: eval_metrics_dict[k] for k in eval_metrics_dict.keys()} def eval_knn_with_model( model, output_dir, train_dataset_str, val_dataset_str, nb_knn=(10, 20, 100, 200), temperature=0.07, autocast_dtype=torch.float, metric_type=MetricType.MEAN_ACCURACY, transform=None, resize_size=256, crop_size=224, batch_size=256, num_workers=5, leave_one_out_dataset="", bag_of_channels=False, avgpool=False, ): autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype) if bag_of_channels: model = BagOfChannelsModelWithNormalize(model, autocast_ctx, avgpool) else: model = ModelWithNormalize(model) if leave_one_out_dataset == "" or leave_one_out_dataset is None: leave_one_out = False else: leave_one_out = True cudnn.benchmark = True transform = make_classification_eval_cell_transform( normalization_type=NormalizationType.SELF_NORM_CENTER_CROP, resize_size=resize_size, crop_size=crop_size ) train_dataset = make_dataset(dataset_str=train_dataset_str, transform=transform) results_dict = {} test_dataset = make_dataset(dataset_str=val_dataset_str, transform=transform) with torch.cuda.amp.autocast(dtype=autocast_dtype): if leave_one_out: results_dict_knn = eval_knn_with_leave_one_out( model=model, leave_one_out_dataset=leave_one_out_dataset, train_dataset=train_dataset, test_dataset=test_dataset, metric_type=metric_type, nb_knn=nb_knn, temperature=temperature, batch_size=batch_size, num_workers=num_workers, ) else: results_dict_knn = eval_knn( model=model, train_dataset=train_dataset, test_dataset=test_dataset, metric_type=metric_type, nb_knn=nb_knn, temperature=temperature, batch_size=batch_size, num_workers=num_workers, ) for knn_ in results_dict_knn.keys(): top1 = results_dict_knn[knn_]["top-1"] results_dict[f"{val_dataset_str}_{knn_} Top 1"] = top1 results_string = f"{val_dataset_str} {knn_} NN classifier result: Top1: {top1:.2f}" if "top-5" in results_dict_knn[knn_]: top5 = results_dict_knn[knn_]["top-5"] results_dict[f"{val_dataset_str}_{knn_} Top 5"] = top5 results_string += f"Top5: {top5:.2f}" logger.info(results_string) metrics_file_path = os.path.join(output_dir, "results_eval_knn.json") with open(metrics_file_path, "a") as f: for k, v in results_dict.items(): f.write(json.dumps({k: v}) + "\n") if distributed.is_enabled(): torch.distributed.barrier() return results_dict def eval_knn( model, train_dataset, test_dataset, metric_type, nb_knn, temperature, batch_size, num_workers, few_shot_eval=False, few_shot_k_or_percent=None, few_shot_n_tries=1, ): num_classes = get_num_classes(train_dataset) train_dataset_dict = create_train_dataset_dict( train_dataset, few_shot_eval=few_shot_eval, few_shot_k_or_percent=few_shot_k_or_percent, few_shot_n_tries=few_shot_n_tries, ) logger.info("Extracting features for train set...") train_data_dict: dict[int, dict[str, torch.Tensor]] = {} for try_n, dataset in train_dataset_dict.items(): features, labels = extract_features_cell_dino(model, dataset, batch_size, num_workers, gather_on_cpu=True) train_data_dict[try_n] = {"train_features": features, "train_labels": labels} test_data_loader = make_data_loader( dataset=DatasetWithEnumeratedTargets( test_dataset, pad_dataset=True, num_replicas=distributed.get_global_size() ), batch_size=batch_size, num_workers=num_workers, sampler_type=SamplerType.DISTRIBUTED, drop_last=False, shuffle=False, persistent_workers=True, collate_fn=None, ) metric_collection = build_metric(metric_type, num_classes=num_classes) device = torch.cuda.current_device() partial_knn_module = partial( KnnModule, T=temperature, device=device, num_classes=num_classes, ) # ============ evaluation ... ============ logger.info("Start the k-NN classification.") eval_metrics_dict = {} for try_ in train_data_dict.keys(): train_features, train_labels = train_data_dict[try_]["train_features"], train_data_dict[try_]["train_labels"] k_list = sorted(set([el if el < len(train_features) else len(train_features) for el in nb_knn])) knn_module = partial_knn_module(train_features=train_features, train_labels=train_labels, nb_knn=k_list) postprocessors, metrics = {k: DictKeysModule([k]) for k in k_list}, { k: metric_collection.clone() for k in k_list } _, eval_metrics, _ = evaluate_with_accumulate( SequentialWithKwargs(model, knn_module), test_data_loader, postprocessors, metrics, device, accumulate_results=False, ) for k in k_list: if k not in eval_metrics_dict: eval_metrics_dict[k] = {} eval_metrics_dict[k][try_] = {metric: v.item() * 100.0 for metric, v in eval_metrics[k].items()} if len(train_data_dict) > 1: return {k: average_metrics(eval_metrics_dict[k]) for k in eval_metrics_dict.keys()} return {k: eval_metrics_dict[k][0] for k in eval_metrics_dict.keys()} def main(args): model, autocast_dtype = setup_and_build_model(args) eval_knn_with_model( model=model, output_dir=args.output_dir, train_dataset_str=args.train_dataset_str, val_dataset_str=args.val_dataset_str, nb_knn=args.nb_knn, temperature=args.temperature, autocast_dtype=autocast_dtype, transform=None, metric_type=args.metric_type, batch_size=args.batch_size, num_workers=5, leave_one_out_dataset=args.leave_one_out_dataset, resize_size=args.resize_size, crop_size=args.crop_size, avgpool=args.avgpool, bag_of_channels=args.bag_of_channels, ) return 0 if __name__ == "__main__": description = "k-NN evaluation on models trained with bag of channel strategy or cell dino" args_parser = get_args_parser(description=description) args = args_parser.parse_args() sys.exit(main(args)) ================================================ FILE: dinov2/eval/cell_dino/linear.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import argparse from functools import partial import json import logging import os import sys from typing import Any, Callable, Dict, Optional, Tuple, List from enum import Enum from dataclasses import dataclass from sklearn.metrics import f1_score import numpy as np import pandas as pd import torch import torch.nn as nn from torch.utils.data import TensorDataset from torch.nn.parallel import DistributedDataParallel from dinov2.data import SamplerType, make_data_loader, make_dataset, DatasetWithEnumeratedTargets from dinov2.data.cell_dino.transforms import NormalizationType, make_classification_eval_cell_transform import dinov2.distributed as distributed from dinov2.eval.metrics import MetricType, build_metric from dinov2.eval.setup import get_args_parser as get_setup_args_parser from dinov2.eval.setup import setup_and_build_model from dinov2.eval.cell_dino.utils import ( evaluate_with_accumulate, LossType, average_metrics, create_train_dataset_dict, get_num_classes, extract_features_for_dataset_dict, ) from dinov2.eval.utils import ModelWithIntermediateLayers from dinov2.logging import MetricLogger from dinov2.utils.checkpoint import build_periodic_checkpointer, resume_or_load logger = logging.getLogger("dinov2") """ List of changes with respect to the standard linear evaluation script: bag of channel option : SCALE ADAPTIVE STRATEGY Adam optimizer instead of SGD Scheduler : two options : onecycleLR or CosineAnnealingLR the transforms/normalization are different, now calling make_classification_eval_cell_transform add binary cross entropy loss option for protein localization change the definition of the num_classes using get_num_classes change of some default parameters (batch_size, epoch_length, epochs, lrs) defined n_last_blocks option avgpool option leave one out strategy for CHAMMI evaluation grid search for optimal weight decay """ def get_args_parser( description: Optional[str] = None, parents: Optional[List[argparse.ArgumentParser]] = None, add_help: bool = True, ): parents = parents or [] setup_args_parser = get_setup_args_parser(parents=parents, add_help=False) parents = [setup_args_parser] parser = argparse.ArgumentParser( description=description, parents=parents, add_help=add_help, ) parser.add_argument( "--train-dataset", dest="train_dataset_str", type=str, help="Training dataset", ) parser.add_argument( "--val-dataset", dest="val_dataset_str", type=str, help="Validation dataset", ) parser.add_argument( "--test-datasets", dest="test_dataset_strs", type=str, nargs="+", help="Test datasets, none to reuse the validation dataset", ) parser.add_argument( "--epochs", type=int, help="Number of training epochs", ) parser.add_argument( "--batch-size", type=int, help="Batch Size (per GPU)", ) parser.add_argument( "--num-workers", type=int, help="Number de Workers", ) parser.add_argument( "--epoch-length", type=int, help="Length of an epoch in number of iterations", ) parser.add_argument( "--save-checkpoint-frequency", type=int, help="Number of epochs between two named checkpoint saves.", ) parser.add_argument( "--eval-period-iterations", type=int, help="Number of iterations between two evaluations.", ) parser.add_argument( "--learning-rates", nargs="+", type=float, help="Learning rates to grid search.", ) parser.add_argument( "--weight_decays", nargs="+", type=float, help="Weight decays to grid search.", ) parser.add_argument( "--n-last-blocks", type=int, help="number of backbone last blocks used for the linear classifier", ) parser.add_argument( "--no-resume", action="store_true", help="Whether to not resume from existing checkpoints", ) parser.add_argument( "--val-metric-type", type=MetricType, choices=list(MetricType), help="Validation metric", ) parser.add_argument( "--test-metric-types", type=MetricType, choices=list(MetricType), nargs="+", help="Evaluation metric", ) parser.add_argument( "--classifier-fpath", type=str, help="Path to a file containing pretrained linear classifiers", ) parser.add_argument( "--val-class-mapping-fpath", type=str, help="Path to a file containing a mapping to adjust classifier outputs", ) parser.add_argument( "--test-class-mapping-fpaths", nargs="+", type=str, help="Path to a file containing a mapping to adjust classifier outputs", ) parser.add_argument( "--loss-type", type=LossType, help="Cross Entropy or Binary Cross Entropy, default cross entropy loss", ) parser.add_argument( "--bag-of-channels", action="store_true", help='Whether to use the "bag of channels" channel adaptive strategy', ) parser.add_argument( "--leave-one-out-dataset", type=str, help="Path with indexes to use the leave one out strategy for CHAMMI_CP task 3 and CHAMMI_HPA task 4", ) parser.add_argument( "--crop-size", type=int, help="crop size for train and eval", ) parser.add_argument( "--resize-size", type=int, help="resize size for image just before crop. 0: no resize", ) parser.add_argument( "--avgpool", action="store_true", help="Whether to use average pooling of path tokens in addition to CLS tokens", ) parser.add_argument( "--scheduler", type=SchedulerType, help="Scheduler type", ) parser.set_defaults( train_dataset_str="ImageNet:split=TRAIN", val_dataset_str="ImageNet:split=VAL", test_dataset_strs=None, epochs=30, batch_size=64, num_workers=8, epoch_length=145, save_checkpoint_frequency=1250, eval_period_iterations=1250, learning_rates=[1e-5, 2e-5, 5e-5, 1e-4, 2e-4, 5e-4, 1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2, 1e-1, 2e-1, 5e-1, 1.0], weight_decays=[0.0, 0.0001, 1.0e-05], val_metric_type=MetricType.MEAN_ACCURACY, test_metric_types=None, classifier_fpath=None, val_class_mapping_fpath=None, test_class_mapping_fpaths=[None], loss_type=LossType.CROSS_ENTROPY, crop_size=384, resize_size=0, n_last_blocks=4, avgpool=False, scheduler=SchedulerType.COSINE_ANNEALING, ) return parser def has_ddp_wrapper(m: nn.Module) -> bool: return isinstance(m, DistributedDataParallel) def remove_ddp_wrapper(m: nn.Module) -> nn.Module: return m.module if has_ddp_wrapper(m) else m def create_linear_input(x_tokens_list, use_n_blocks, use_avgpool, bag_of_channels): intermediate_output = x_tokens_list[-use_n_blocks:] output = torch.cat([class_token for _, class_token in intermediate_output], dim=-1) if bag_of_channels: if use_avgpool: output = torch.cat( ( output, torch.mean(intermediate_output[-1][0], dim=-2).reshape(intermediate_output[-1][0].shape[0], -1), # average pooling of patch tokens: average over N, then concatenate channels if single-channel patch model ), dim=-1, ) # concatenate average pooling of patch tokens to concatenated patch tokens else: if use_avgpool: output = torch.cat( ( output, torch.mean(intermediate_output[-1][0], dim=1), # patch tokens ), dim=-1, ) output = output.reshape(output.shape[0], -1) return output.float() class LinearClassifier(nn.Module): """Linear layer to train on top of frozen features""" def __init__( self, out_dim, use_n_blocks, use_avgpool, num_classes=1000, bag_of_channels=False, leave_one_out=False ): super().__init__() self.out_dim = out_dim self.use_n_blocks = use_n_blocks self.use_avgpool = use_avgpool self.num_classes = num_classes self.bag_of_channels = bag_of_channels self.leave_one_out = leave_one_out self.linear = nn.Linear(out_dim, num_classes) self.linear.weight.data.normal_(mean=0.0, std=0.01) self.linear.bias.data.zero_() def forward(self, x_tokens_list): if self.leave_one_out: return self.linear(x_tokens_list) output = create_linear_input(x_tokens_list, self.use_n_blocks, self.use_avgpool, self.bag_of_channels) return self.linear(output) class AllClassifiers(nn.Module): def __init__(self, classifiers_dict): super().__init__() self.classifiers_dict = nn.ModuleDict() self.classifiers_dict.update(classifiers_dict) def forward(self, inputs): return {k: v.forward(inputs) for k, v in self.classifiers_dict.items()} def __len__(self): return len(self.classifiers_dict) class LinearPostprocessor(nn.Module): def __init__(self, linear_classifier, class_mapping=None): super().__init__() self.linear_classifier = linear_classifier self.register_buffer("class_mapping", None if class_mapping is None else torch.LongTensor(class_mapping)) def forward(self, samples, targets): preds = self.linear_classifier(samples) return { "preds": preds[:, self.class_mapping] if self.class_mapping is not None else preds, "target": targets, } def scale_lr(learning_rates, batch_size): return learning_rates * (batch_size * distributed.get_global_size()) / 256.0 def setup_linear_classifiers( sample_output, n_last_blocks_list, learning_rates, weight_decays, batch_size, num_classes=1000, bag_of_channels=False, leave_one_out=False, avgpool=False, ): linear_classifiers_dict = nn.ModuleDict() avgpool_value = avgpool optim_param_groups = [] for n in n_last_blocks_list: for avgpool in [avgpool_value]: for _lr in learning_rates: for wd in weight_decays: lr = scale_lr(_lr, batch_size) out_dim = create_linear_input( sample_output, use_n_blocks=n, use_avgpool=avgpool, bag_of_channels=bag_of_channels ).shape[1] linear_classifier = LinearClassifier( out_dim, use_n_blocks=n, use_avgpool=avgpool, num_classes=num_classes, bag_of_channels=bag_of_channels, leave_one_out=leave_one_out, ) linear_classifier = linear_classifier.cuda() linear_classifiers_dict[ f"classifier_{n}_blocks_avgpool_{avgpool}_lr_{lr:.5f}_wd_{wd:.2E}".replace(".", "_") ] = linear_classifier optim_param_groups.append({"params": linear_classifier.parameters(), "lr": lr, "weight_decay": wd}) linear_classifiers = AllClassifiers(linear_classifiers_dict) if distributed.is_enabled(): linear_classifiers = nn.parallel.DistributedDataParallel(linear_classifiers) return linear_classifiers, optim_param_groups def make_eval_data_loader( *, test_dataset_str_or_path_or_loo_dataset, config, batch_size, num_workers, ): if isinstance(test_dataset_str_or_path_or_loo_dataset, str): logger.info(f"Loading dataset {test_dataset_str_or_path_or_loo_dataset}") transform = make_classification_eval_cell_transform( normalization_type=NormalizationType.SELF_NORM_CENTER_CROP, resize_size=config["resize_size"], crop_size=config["crop_size"], ) test_dataset = make_dataset(dataset_str=test_dataset_str_or_path_or_loo_dataset, transform=transform) collate_fn = None else: logger.info("Making data loader for feature dataset (typical in leave one out evaluation)") test_dataset = test_dataset_str_or_path_or_loo_dataset collate_fn = None class_mapping = None if hasattr(test_dataset, "get_imagenet_class_mapping"): class_mapping = test_dataset.get_imagenet_class_mapping() test_data_loader = make_data_loader( dataset=DatasetWithEnumeratedTargets( test_dataset, pad_dataset=True, num_replicas=distributed.get_global_size() ), batch_size=batch_size, num_workers=num_workers, sampler_type=SamplerType.DISTRIBUTED, drop_last=False, shuffle=False, persistent_workers=False, collate_fn=collate_fn, ) return test_data_loader, class_mapping @dataclass class Evaluator: batch_size: int num_workers: int dataset_str_or_path: str config: Dict metric_type: MetricType metrics_file_path: str training_num_classes: int save_results_func: Optional[Callable] val_dataset_loo: Optional[TensorDataset] = None def __post_init__(self): self.main_metric_name = f"{self.dataset_str_or_path}_accuracy" if self.val_dataset_loo is not None: self.dataset_str_or_path = self.val_dataset_loo self.data_loader, self.class_mapping = make_eval_data_loader( test_dataset_str_or_path_or_loo_dataset=self.dataset_str_or_path, batch_size=self.batch_size, num_workers=self.num_workers, config=self.config, ) @torch.no_grad() def _evaluate_linear_classifiers( self, *, feature_model, linear_classifiers, iteration, prefixstring="", best_classifier_on_val=None, accumulate_results=False, test_mode=False, ) -> Tuple[Dict[str, Any], Optional[Dict[str, torch.Tensor]]]: logger.info("running validation !") num_classes = len(self.class_mapping) if self.class_mapping is not None else self.training_num_classes metric = build_metric(self.metric_type, num_classes=num_classes) postprocessors = { k: LinearPostprocessor(v, self.class_mapping) for k, v in linear_classifiers.classifiers_dict.items() } metrics = {k: metric.clone() for k in linear_classifiers.classifiers_dict} _, results_dict_temp, accumulated_results = evaluate_with_accumulate( feature_model, self.data_loader, postprocessors, metrics, torch.cuda.current_device(), accumulate_results=accumulate_results, leave_one_out=self.config["leave_one_out"], test_mode=test_mode, ) logger.info("") results_dict = {} max_accuracy = 0 best_classifier = "" for _, (classifier_string, metric) in enumerate(results_dict_temp.items()): logger.info(f"{prefixstring} -- Classifier: {classifier_string} * {metric}") if ( best_classifier_on_val is None and metric["top-1"].item() > max_accuracy ) or classifier_string == best_classifier_on_val: max_accuracy = metric["top-1"].item() best_classifier = classifier_string results_dict["best_classifier"] = {"name": best_classifier, "accuracy": max_accuracy} logger.info(f"best classifier: {results_dict['best_classifier']}") accumulated_best_results = None if test_mode: accumulated_best_results = accumulated_results elif accumulated_results is not None: accumulated_best_results = accumulated_results[best_classifier] if distributed.is_main_process(): with open(self.metrics_file_path, "a") as f: f.write(f"iter: {iteration}\n") for k, v in results_dict.items(): f.write(json.dumps({k: v}) + "\n") f.write("\n") return results_dict, accumulated_best_results def evaluate_and_maybe_save( self, feature_model, linear_classifiers, iteration: int, best_classifier_on_val: Optional[Any] = None, save_filename_suffix: str = "", prefixstring: str = "", test_mode: bool = False, ): logger.info(f"Testing on {self.dataset_str_or_path}") save_results = self.save_results_func is not None full_results_dict, accumulated_best_results = self._evaluate_linear_classifiers( feature_model=feature_model, linear_classifiers=remove_ddp_wrapper(linear_classifiers), iteration=iteration, prefixstring=prefixstring, best_classifier_on_val=best_classifier_on_val, accumulate_results=save_results, test_mode=test_mode, ) if self.save_results_func is not None: self.save_results_func( filename_suffix=f"{self.dataset_str_or_path}{save_filename_suffix}", **accumulated_best_results ) results_dict = { self.main_metric_name: 100.0 * full_results_dict["best_classifier"]["accuracy"], "best_classifier": full_results_dict["best_classifier"]["name"], } return results_dict, accumulated_best_results def make_evaluators( config: Dict, val_metric_type: MetricType, val_dataset: str, metric_type: MetricType, metrics_file_path: str, training_num_classes: int, save_results_func: Optional[Callable], val_dataset_loo: Optional[TensorDataset] = None, ): test_metric_types = config["test_metric_types"] test_dataset_strs = config["test_datasets"] if test_dataset_strs is None: test_dataset_strs = (config["val_dataset"],) if test_metric_types is None: test_metric_types = (val_metric_type,) else: assert len(test_metric_types) == len(config["test_datasets"]) val_evaluator, *test_evaluators = [ Evaluator( dataset_str_or_path=dataset_str_or_path, batch_size=config["batch_size"], num_workers=config["num_workers"], config=config, metric_type=metric_type, metrics_file_path=metrics_file_path, training_num_classes=training_num_classes, save_results_func=save_results_func, val_dataset_loo=val_dataset_loo, ) for dataset_str_or_path, metric_type in zip( (val_dataset,) + tuple(test_dataset_strs), (val_metric_type,) + tuple(test_metric_types), ) ] return val_evaluator, test_evaluators class SchedulerType(Enum): COSINE_ANNEALING = "cosine_annealing" ONE_CYCLE = "one_cycle" def get_scheduler(self, optimizer, optim_param_groups, epoch_length, epochs, max_iter): if self == SchedulerType.ONE_CYCLE: lr_list = [optim_param_groups[i]["lr"] for i in range(len(optim_param_groups))] scheduler = torch.optim.lr_scheduler.OneCycleLR( optimizer, max_lr=lr_list, steps_per_epoch=epoch_length, epochs=epochs ) else: scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_iter, eta_min=0) print("CosineAnnealingLR scheduler") return scheduler def setup_linear_training( *, config: Dict, sample_output: torch.Tensor, training_num_classes: int, checkpoint_output_dir: str, ): linear_classifiers, optim_param_groups = setup_linear_classifiers( sample_output, config["n_last_blocks_list"], config["learning_rates"], config["weight_decays"], config["batch_size"], training_num_classes, config["bag_of_channels"], config["leave_one_out"], config["avgpool"], ) max_iter = config["epochs"] * config["epoch_length"] optimizer = torch.optim.AdamW(optim_param_groups, weight_decay=0) scheduler = config["scheduler"].get_scheduler( optimizer=optimizer, optim_param_groups=optim_param_groups, epoch_length=config["epoch_length"], epochs=config["epochs"], max_iter=max_iter, ) checkpoint_period = config["save_checkpoint_iterations"] or config["epoch_length"] periodic_checkpointer = build_periodic_checkpointer( linear_classifiers, checkpoint_output_dir, optimizer=optimizer, scheduler=scheduler, period=checkpoint_period, max_iter=max_iter, max_to_keep=None, ) checkpoint = resume_or_load(periodic_checkpointer, config["classifier_fpath"] or "", resume=config["resume"]) start_iter = checkpoint.get("iteration", -1) + 1 best_accuracy = checkpoint.get("best_accuracy", -1) if config["loss_type"] == LossType.BINARY_CROSS_ENTROPY: criterion = nn.BCEWithLogitsLoss() else: criterion = nn.CrossEntropyLoss() return ( linear_classifiers, start_iter, max_iter, criterion, optimizer, scheduler, periodic_checkpointer, best_accuracy, ) def train_linear_classifiers( *, feature_model, train_dataset, train_config: Dict, training_num_classes: int, val_evaluator: Evaluator, checkpoint_output_dir: str, sample_output: Optional[torch.Tensor] = None, ): if train_config["leave_one_out"]: assert sample_output is not None, "sample_output should be passed as argument when using leave_one_out." else: sample_output = feature_model(train_dataset[0][0].unsqueeze(0).cuda()) ( linear_classifiers, start_iter, max_iter, criterion, optimizer, scheduler, periodic_checkpointer, best_accuracy, ) = setup_linear_training( config=train_config, sample_output=sample_output, training_num_classes=training_num_classes, checkpoint_output_dir=checkpoint_output_dir, ) sampler_type = SamplerType.INFINITE train_data_loader = make_data_loader( dataset=train_dataset, batch_size=train_config["batch_size"], num_workers=train_config["num_workers"], shuffle=True, seed=0, sampler_type=sampler_type, sampler_advance=start_iter, drop_last=True, persistent_workers=True, ) eval_period = train_config["eval_period_iterations"] or train_config["epoch_length"] iteration = start_iter logger.info("Starting training from iteration {}".format(start_iter)) metric_logger = MetricLogger(delimiter=" ") header = "Training" for data, labels in metric_logger.log_every( train_data_loader, 10, header, max_iter, start_iter, ): data = data.cuda(non_blocking=True) labels = labels.cuda(non_blocking=True) if not train_config["leave_one_out"]: in_classifier = feature_model(data) else: in_classifier = data outputs = linear_classifiers(in_classifier) if len(labels.shape) > 1: labels = labels.float() losses = {f"loss_{k}": criterion(v, labels) for k, v in outputs.items()} loss = sum(losses.values()) optimizer.zero_grad() loss.backward() optimizer.step() scheduler.step() if iteration % 10 == 0: torch.cuda.synchronize() metric_logger.update(loss=loss.item()) metric_logger.update(lr=optimizer.param_groups[0]["lr"]) periodic_checkpointer.step(iteration=iteration, best_accuracy=best_accuracy) if eval_period > 0 and (iteration + 1) % eval_period == 0 and iteration != max_iter - 1: val_results_dict, _ = val_evaluator.evaluate_and_maybe_save( feature_model=feature_model, linear_classifiers=linear_classifiers, prefixstring=f"ITER: {iteration}", iteration=iteration, ) val_accuracy = val_results_dict[val_evaluator.main_metric_name] if val_accuracy >= best_accuracy: best_accuracy = val_accuracy periodic_checkpointer.save_best(iteration=iteration, best_accuracy=best_accuracy) torch.distributed.barrier() iteration = iteration + 1 return feature_model, linear_classifiers, iteration, periodic_checkpointer def eval_linear_with_model( model, output_dir, train_dataset_str, val_dataset_str, batch_size, epochs, epoch_length, num_workers, save_checkpoint_frequency, eval_period_iterations, learning_rates, weight_decays, autocast_dtype, test_dataset_strs=None, resume=True, classifier_fpath=None, val_metric_type=MetricType.MEAN_ACCURACY, test_metric_types=None, loss_type=LossType.CROSS_ENTROPY, bag_of_channels=False, leave_one_out_dataset="", resize_size=0, crop_size=384, n_last_blocks=4, avgpool=False, scheduler=SchedulerType.COSINE_ANNEALING, ): if leave_one_out_dataset == "" or leave_one_out_dataset is None: leave_one_out = False else: logger.info("Reading the leave-one-out label metadata.") leave_one_out_indices = {} metadata = pd.read_csv(leave_one_out_dataset) if "HPA" in leave_one_out_dataset: metadata = metadata[metadata["Task_three"]].reset_index() leave_one_out_label_type = "cell_type" else: metadata = metadata[metadata["Task_four"]].reset_index() leave_one_out_label_type = "Plate" leave_one_out_labels = metadata[leave_one_out_label_type].unique() for leave_one_out_label in leave_one_out_labels: leave_one_out_indices[leave_one_out_label] = np.array( metadata[metadata[leave_one_out_label_type] == leave_one_out_label].index.values ) leave_one_out = True train_transform = make_classification_eval_cell_transform( normalization_type=NormalizationType.SELF_NORM_AUG_DECODER, crop_size=crop_size, resize_size=resize_size ) print("train_transform", train_transform) train_dataset = make_dataset( dataset_str=train_dataset_str, transform=train_transform, ) training_num_classes = get_num_classes(train_dataset) if leave_one_out: training_num_classes += train_dataset.num_additional_labels_loo_eval train_dataset_dict = create_train_dataset_dict(train_dataset) n_last_blocks_list = [n_last_blocks] n_last_blocks = max(n_last_blocks_list) dataset_use_cache = True autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype) feature_model = ModelWithIntermediateLayers(model, n_last_blocks, autocast_ctx) if bag_of_channels: sample = train_dataset[0][0].unsqueeze(0) sample_output = feature_model(sample.cuda()) if leave_one_out: loo_dict = {} train_data_dict = extract_features_for_dataset_dict( feature_model, train_dataset_dict, batch_size, num_workers, gather_on_cpu=True, avgpool=avgpool, ) val_dataset = make_dataset( dataset_str=val_dataset_str, transform=make_classification_eval_cell_transform( normalization_type=NormalizationType.SELF_NORM_CENTER_CROP, crop_size=crop_size, resize_size=resize_size ), ) val_dataset_dict = create_train_dataset_dict(val_dataset) val_data_dict = extract_features_for_dataset_dict( feature_model, val_dataset_dict, batch_size, num_workers, gather_on_cpu=True, avgpool=avgpool, ) train_features = train_data_dict[0]["train_features"] train_labels = train_data_dict[0]["train_labels"] val_features = val_data_dict[0]["train_features"] val_labels = val_data_dict[0]["train_labels"] for loo_label, loo_indices in leave_one_out_indices.items(): loo_for_training_indices = torch.ones(val_features.shape[0], dtype=bool) loo_for_training_indices[loo_indices] = False loo_for_val_indices = torch.zeros(val_features.shape[0], dtype=bool) loo_for_val_indices[loo_indices] = True loo_dict[loo_label] = { "train_features": torch.cat([train_features, val_features[loo_for_training_indices]]), "train_labels": torch.cat([train_labels, val_labels[loo_for_training_indices]]), "val_features": val_features[loo_indices], "val_labels": val_labels[loo_indices], } save_results_func = None # if config.save_results: # save_results_func = partial(default_save_results_func, output_dir=output_dir) metrics_file_path = os.path.join(output_dir, "results_eval_linear.json") periodic_checkpointers: list = [] train_config = { "learning_rates": learning_rates, "weight_decays": weight_decays, "batch_size": batch_size, "num_workers": num_workers, "dataset_use_cache": dataset_use_cache, "eval_period_iterations": eval_period_iterations, "epoch_length": epoch_length, "leave_one_out": leave_one_out, "bag_of_channels": bag_of_channels, "n_last_blocks_list": n_last_blocks_list, "epochs": epochs, "loss_type": loss_type, "resume": resume, "save_checkpoint_iterations": save_checkpoint_frequency, "classifier_fpath": classifier_fpath, "avgpool": avgpool, "scheduler": scheduler, } config = { "test_metric_types": test_metric_types, "test_datasets": test_dataset_strs, "val_metric_types": val_metric_type, "val_dataset": val_dataset_str, "batch_size": batch_size, "num_workers": num_workers, "leave_one_out": leave_one_out, "crop_size": crop_size, "resize_size": resize_size, } if not leave_one_out: val_evaluator, test_evaluators = make_evaluators( config=config, val_metric_type=val_metric_type, val_dataset=val_dataset_str, metric_type=test_metric_types, metrics_file_path=metrics_file_path, training_num_classes=training_num_classes, save_results_func=save_results_func, ) results_dict = {} for _try in train_dataset_dict.keys(): if len(train_dataset_dict) > 1: checkpoint_output_dir = os.path.join(output_dir, f"checkpoints_{_try}") save_filename_suffix = f"_{_try}" else: checkpoint_output_dir, save_filename_suffix = output_dir, "" os.makedirs(checkpoint_output_dir, exist_ok=True) feature_model, linear_classifiers, iteration, periodic_checkpointer = train_linear_classifiers( train_config=train_config, feature_model=feature_model, train_dataset=train_dataset_dict[_try], training_num_classes=training_num_classes, val_evaluator=val_evaluator, checkpoint_output_dir=checkpoint_output_dir, ) periodic_checkpointers.append(periodic_checkpointer) results_dict[_try], _ = val_evaluator.evaluate_and_maybe_save( feature_model=feature_model, linear_classifiers=linear_classifiers, iteration=iteration, save_filename_suffix=save_filename_suffix, ) for test_evaluator in test_evaluators: eval_results_dict, _ = test_evaluator.evaluate_and_maybe_save( feature_model=feature_model, linear_classifiers=linear_classifiers, iteration=iteration, best_classifier_on_val=results_dict[_try]["best_classifier"], save_filename_suffix=save_filename_suffix, ) results_dict[_try] = {**eval_results_dict, **results_dict[_try]} if len(train_dataset_dict) > 1: results_dict = average_metrics(results_dict, ignore_keys=["best_classifier"]) else: results_dict = {**results_dict[_try]} else: # if leave one out is True test_results_dict = {} for loo_label in loo_dict.keys(): checkpoint_output_dir, save_filename_suffix = os.path.join(output_dir, f"checkpoints_{loo_label}"), "" os.makedirs(checkpoint_output_dir, exist_ok=True) train_dataset_loo = TensorDataset( loo_dict[loo_label]["train_features"], loo_dict[loo_label]["train_labels"] ) logger.info(f"Creating leave_one_out evaluators. loo_label: {loo_label}") val_dataset_loo = TensorDataset(loo_dict[loo_label]["val_features"], loo_dict[loo_label]["val_labels"]) val_evaluators_loo, _ = make_evaluators( config=config, val_metric_type=val_metric_type, val_dataset="loo", metric_type=test_metric_types, metrics_file_path=metrics_file_path, training_num_classes=training_num_classes, save_results_func=save_results_func, val_dataset_loo=val_dataset_loo, ) feature_model, linear_classifiers, iteration, periodic_checkpointer = train_linear_classifiers( feature_model=feature_model, train_dataset=train_dataset_loo, train_config=train_config, training_num_classes=training_num_classes, val_evaluator=val_evaluators_loo, checkpoint_output_dir=checkpoint_output_dir, sample_output=sample_output, ) periodic_checkpointers.append(periodic_checkpointer) _, test_results_dict[loo_label] = val_evaluators_loo.evaluate_and_maybe_save( feature_model=feature_model, linear_classifiers=linear_classifiers, iteration=iteration, save_filename_suffix=save_filename_suffix, test_mode=True, ) classifier_names = test_results_dict[loo_label].keys() results_dict = {k: [[], []] for k in classifier_names} for ll in test_results_dict.keys(): for k in classifier_names: results_dict[k][0].append(test_results_dict[ll][k][0]) results_dict[k][1].append(test_results_dict[ll][k][1]) for k in classifier_names: results_dict[k] = [ np.argmax(torch.cat(results_dict[k][0]).cpu().detach().numpy(), axis=1), torch.cat(results_dict[k][1]).cpu().detach().numpy(), ] results_dict[k] = f1_score(results_dict[k][1], results_dict[k][0], average="macro", labels=[4, 5, 6]) logger.info( f"Best performance is for {max(results_dict, key=results_dict.get)}, with F1-Score of {results_dict[max(results_dict, key=results_dict.get)]}" ) logger.info("Test Results Dict " + str(results_dict)) return results_dict def main(args): model, autocast_dtype = setup_and_build_model(args) eval_linear_with_model( model=model, output_dir=args.output_dir, train_dataset_str=args.train_dataset_str, val_dataset_str=args.val_dataset_str, test_dataset_strs=args.test_dataset_strs, batch_size=args.batch_size, epochs=args.epochs, epoch_length=args.epoch_length, num_workers=args.num_workers, save_checkpoint_frequency=args.save_checkpoint_frequency, eval_period_iterations=args.eval_period_iterations, learning_rates=args.learning_rates, weight_decays=args.weight_decays, autocast_dtype=autocast_dtype, resume=not args.no_resume, classifier_fpath=args.classifier_fpath, val_metric_type=args.val_metric_type, test_metric_types=args.test_metric_types, loss_type=args.loss_type, bag_of_channels=args.bag_of_channels, leave_one_out_dataset=args.leave_one_out_dataset, crop_size=args.crop_size, resize_size=args.resize_size, n_last_blocks=args.n_last_blocks, avgpool=args.avgpool, scheduler=args.scheduler, ) return 0 if __name__ == "__main__": description = "DINOv2 linear_cell_dino evaluation" args_parser = get_args_parser(description=description) args = args_parser.parse_args() sys.exit(main(args)) ================================================ FILE: dinov2/eval/cell_dino/utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import logging from typing import Callable, Dict, Optional, Any, List import torch from torch import nn from torchmetrics import MetricCollection from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader from dinov2.data import NoOpAccumulator, ResultsAccumulator import dinov2.distributed as distributed from dinov2.logging import MetricLogger from enum import Enum from torch.utils.data import Subset from torchvision.datasets.vision import StandardTransform import numpy as np from torch.nn.functional import one_hot, softmax logger = logging.getLogger("dinov2") class LossType(Enum): CROSS_ENTROPY = "cross_entropy" BINARY_CROSS_ENTROPY = "binary_cross_entropy" class BagOfChannelsModelWithNormalize(nn.Module): def __init__(self, model, autocast_ctx, avgpool, n_last_blocks=1): super().__init__() self.model = model self.autocast_ctx = autocast_ctx self.n_last_blocks = n_last_blocks self.avgpool = avgpool def forward(self, samples): with self.autocast_ctx(): features = self.model.get_intermediate_layers(samples, self.n_last_blocks, return_class_token=True) output = create_linear_input(features, self.avgpool, use_n_blocks=self.n_last_blocks) return nn.functional.normalize(output, dim=1, p=2) @torch.inference_mode() def evaluate_with_accumulate( model: nn.Module, data_loader, postprocessors: Dict[str, nn.Module], metrics: Dict[str, MetricCollection], device: torch.device, criterion: Optional[nn.Module] = None, test_mode: bool = False, accumulate_results: bool = False, leave_one_out: bool = False, ): model.eval() if test_mode: output_tensor = {k: [] for k in postprocessors.keys()} target_tensor = {k: [] for k in postprocessors.keys()} if criterion is not None: criterion.eval() accumulator_class = ResultsAccumulator if accumulate_results else NoOpAccumulator accumulators = {k: accumulator_class() for k in postprocessors.keys()} for metric in metrics.values(): metric = metric.to(device) metric_logger = MetricLogger(delimiter=" ") header = "Test:" for samples, targets, *_ in metric_logger.log_every(data_loader, 10, header): if isinstance(targets, list): index = targets[0] targets = targets[1] samples, targets, index = samples[index >= 0], targets[index >= 0], index[index >= 0] if len(index) == 0: continue outputs = samples.to(device) if leave_one_out else model(samples.to(device)) targets = targets.to(device) if criterion is not None: loss = criterion(outputs, targets) metric_logger.update(loss=loss.item()) for k, metric in metrics.items(): metric_inputs = postprocessors[k](outputs, targets) metric.update(**metric_inputs) if test_mode: output_tensor[k].append(metric_inputs["preds"]) target_tensor[k].append(metric_inputs["target"]) accumulators[k].update(preds=metric_inputs["preds"], target=metric_inputs["target"], index=index) metric_logger.synchronize_between_processes() logger.info(f"Averaged stats: {metric_logger}") stats = {k: metric.compute() for k, metric in metrics.items()} metric_logger_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} # accumulator.accumulate() returns None for the NoOpAccumulator accumulated_results = {k: accumulator.accumulate() for k, accumulator in accumulators.items()} if test_mode: for k in postprocessors.keys(): output_tensor[k] = torch.cat(output_tensor[k]) target_tensor[k] = torch.cat(target_tensor[k]) accumulated_results = {k: [output_tensor[k], target_tensor[k]] for k in postprocessors.keys()} if accumulate_results: return metric_logger_stats, stats return metric_logger_stats, stats, accumulated_results def all_gather_and_flatten(tensor_rank): tensor_all_ranks = torch.empty( distributed.get_global_size(), *tensor_rank.shape, dtype=tensor_rank.dtype, device=tensor_rank.device, ) tensor_list = list(tensor_all_ranks.unbind(0)) torch.distributed.all_gather(tensor_list, tensor_rank.contiguous()) return tensor_all_ranks.flatten(end_dim=1) def extract_features_cell_dino( model, dataset, batch_size, num_workers, gather_on_cpu=False, shuffle=False, avgpool=False ): dataset_with_enumerated_targets = DatasetWithEnumeratedTargets(dataset) sample_count = len(dataset_with_enumerated_targets) data_loader = make_data_loader( dataset=dataset_with_enumerated_targets, batch_size=batch_size, num_workers=num_workers, sampler_type=SamplerType.DISTRIBUTED, drop_last=False, shuffle=shuffle, ) return extract_features_with_dataloader_cell_dino(model, data_loader, sample_count, gather_on_cpu, avgpool=avgpool) @torch.inference_mode() def extract_features_with_dataloader_cell_dino(model, data_loader, sample_count, gather_on_cpu=False, avgpool=False): gather_device = torch.device("cpu") if gather_on_cpu else torch.device("cuda") metric_logger = MetricLogger(delimiter=" ") features, all_labels = None, None for samples, (index, labels_rank) in metric_logger.log_every(data_loader, 10): samples = samples.cuda(non_blocking=True) labels_rank = labels_rank.cuda(non_blocking=True) index = index.cuda(non_blocking=True) feat = model(samples) if isinstance(samples, list) or isinstance(feat, tuple): features_rank = create_linear_input(feat, avgpool=avgpool) else: features_rank = feat # init storage feature matrix if features is None: features = torch.zeros(sample_count, features_rank.shape[-1], device=gather_device) labels_shape = list(labels_rank.shape) labels_shape[0] = sample_count all_labels = torch.full(labels_shape, fill_value=-1, device=gather_device) logger.info(f"Storing features into tensor of shape {features.shape}") # share indexes, features and labels between processes index_all = all_gather_and_flatten(index).to(gather_device) features_all_ranks = all_gather_and_flatten(features_rank).to(gather_device) labels_all_ranks = all_gather_and_flatten(labels_rank).to(gather_device) # update storage feature matrix if len(index_all) > 0: features.index_copy_(0, index_all, features_all_ranks) all_labels.index_copy_(0, index_all, labels_all_ranks) logger.info(f"Features shape: {tuple(features.shape)}") logger.info(f"Labels shape: {tuple(all_labels.shape)}") assert torch.all(all_labels > -1) return features, all_labels def create_linear_input(x_tokens_list, avgpool=False, use_n_blocks=1): intermediate_output = x_tokens_list[-use_n_blocks:] output = torch.cat( [class_token for _, class_token in intermediate_output], dim=-1 ) # concatenate class tokens of the last n blocks if avgpool: output = torch.cat( ( output, torch.mean(intermediate_output[-1][0], dim=-2).reshape( intermediate_output[-1][0].shape[0], -1 ), # average pooling of patch tokens: average over N, then concatenate channels if single-channel patch model ), dim=-1, ) # concatenate average pooling of patch tokens to concatenated patch tokens output = output.reshape(output.shape[0], -1) return output.float() def get_target_transform(dataset) -> Optional[Callable]: if hasattr(dataset, "transforms"): if isinstance(dataset.transforms, StandardTransform): return dataset.transforms.target_transform raise ValueError("Dataset has a non-standard .transforms property") if hasattr(dataset, "target_transform"): return dataset.target_transform return None def get_labels(dataset) -> torch.Tensor: """ Get the labels of a classification dataset, as a Tensor, using the `get_targets` method if it is present or loading the labels one by one with `get_target`, if it exists. If the dataset has a target transform, iterate over the whole dataset to get the transformed labels for each element, then stack them as a torch tensor. """ logger.info("Getting dataset labels ...") if hasattr(dataset, "get_targets") or hasattr(dataset, "get_target"): if hasattr(dataset, "get_targets"): # Returns a np.array labels = dataset.get_targets() elif hasattr(dataset, "get_target"): labels = [dataset.get_target(i) for i in range(len(dataset))] target_transform = get_target_transform(dataset) if target_transform is not None: labels = [target_transform(label) for label in labels] else: # Target transform is applied in this case labels = [dataset[i][1] for i in range(len(dataset))] return torch.stack([torch.tensor(label, dtype=int) for label in labels]) def get_num_classes(dataset) -> int: """ Get the labels of a dataset and compute the number of classes """ labels = get_labels(dataset) if len(labels.shape) > 1: return int(labels.shape[1]) return int(labels.max() + 1) def average_metrics(eval_metrics_dict: dict[Any, dict[str, torch.Tensor]], ignore_keys: List[str] = []): """ Function that computes the average and the std on a metrics dict. A linear evaluation dictionary contains "best_classifier", so this specific key is removed for computing aggregated metrics. """ output_metrics_dict = {} metrics = [metric for metric in eval_metrics_dict[0].keys() if metric not in ignore_keys] for metric in metrics: stats_tensor = torch.tensor([stat[metric] for stat in eval_metrics_dict.values()]) output_metrics_dict[metric + "_mean"] = stats_tensor.mean().item() output_metrics_dict[metric + "_std"] = torch.std(stats_tensor).item() return output_metrics_dict def create_class_indices_mapping(labels: torch.Tensor) -> dict[int, torch.Tensor]: """ Efficiently creates a mapping between the labels and tensors containing the indices of all the dataset elements that share this label. In the case of multiple labels, it is not guaranteed that there will be exactly the specified percentage of labels. """ if len(labels.shape) > 1: # labels are a one-hot encoding assert len(labels.shape) == 2 sorted_labels, indices = torch.nonzero(labels.T, as_tuple=True) else: sorted_labels, indices = torch.sort(labels, stable=True) unique_labels, counts = torch.unique_consecutive(sorted_labels, return_counts=True) mapping = dict(zip(unique_labels.tolist(), torch.split(indices, counts.tolist()))) return mapping def _shuffle_dataset(dataset: torch.Tensor, seed: int = 0): """ Shuffling a dataset by subsetting it with a random permutation of its indices """ random_generator = torch.Generator() random_generator.manual_seed(seed) random_indices = torch.randperm(len(dataset), generator=random_generator) return Subset(dataset, random_indices) def _subset_dataset_per_class( class_indices_mapping: dict[int, torch.Tensor], n_or_percent_per_class: float, dataset_size: int, seed: int = 0, is_percent: bool = False, ) -> torch.Tensor: """ Helper function to select a percentage of a dataset, equally distributed across classes, or to take the same number of elements from each class of the dataset. Returns a boolean mask tensor being True at indices of selected elements """ random_generator = torch.Generator() random_generator.manual_seed(seed) final_indices_bool = torch.zeros(dataset_size, dtype=bool) for class_indices in class_indices_mapping.values(): # Select at least one element n_for_class = max(int(len(class_indices) * n_or_percent_per_class), 1) if is_percent else n_or_percent_per_class assert isinstance(n_for_class, int) filtered_index = torch.randperm(len(class_indices), generator=random_generator)[:n_for_class] final_indices_bool[class_indices[filtered_index]] = True return final_indices_bool def _multilabel_rebalance_subset( class_indices_mapping: dict[int, torch.Tensor], n_or_percent_per_class: float, labels: torch.Tensor, indices_bool: torch.Tensor, dataset_size: int, seed: int = 0, ) -> torch.Tensor: """ Helper function to refine a subset of a multi-label dataset (indices_bool) to better match a target percentage of labels. Returns a boolean mask tensor being True at indices of selected elements. """ # Compute the number of selected labels in indices_bool num_total_labels = labels.sum() num_wanted_labels = int(num_total_labels * n_or_percent_per_class) num_selected_labels = (labels[indices_bool] > 0).sum() logger.info(f" {num_selected_labels} labels instead of {num_wanted_labels}") # Compute a new percentage and new set selecting less images, therefore less labels, to match approximatelly the exact percentage of labels selected n_or_percent_per_class = n_or_percent_per_class / (num_selected_labels / num_wanted_labels) final_indices_bool = _subset_dataset_per_class( class_indices_mapping, n_or_percent_per_class, dataset_size, seed, True ) # Compute the number of labels finally used num_selected_labels = (labels[final_indices_bool] > 0).sum() logger.info(f" {num_selected_labels} labels instead of {num_wanted_labels}") return final_indices_bool def split_train_val_datasets(train_dataset, split_percentage: float = 0.1, shuffle_train: bool = True): """ Splitting a percent of the train dataset to choose hyperparameters, taking the same percentage for each class. If `shuffle` is False, taking the first elements of each class as the validaton set. """ assert 0 < split_percentage < 1 logger.info(f"Selecting {int(split_percentage * 100)}% of the train dataset as the validation set") if shuffle_train: logger.info("Shuffling train dataset before splitting in train and validation sets") train_dataset = _shuffle_dataset(train_dataset) train_labels = get_labels(train_dataset) class_indices_mapping = create_class_indices_mapping(train_labels) val_mask = torch.zeros(len(train_labels), dtype=bool) for class_indices in class_indices_mapping.values(): # If there is only one element, it goes in the train set n_for_val = max(1, int(split_percentage * len(class_indices))) if len(class_indices) > 1 else 0 val_mask[class_indices[:n_for_val]] = True val_dataset = Subset(train_dataset, val_mask.nonzero().flatten()) train_dataset = Subset(train_dataset, (~val_mask).nonzero().flatten()) return train_dataset, val_dataset def create_train_dataset_dict( train_dataset, few_shot_eval: bool = False, few_shot_k_or_percent=None, few_shot_n_tries: int = 1, ) -> dict[int, dict[int, Any]]: """ Randomly split a dataset for few-shot evaluation, with `few_shot_k_or_percent` being n elements or x% of a class. Produces a dict, which keys are number of random "tries" and values are the dataset subset for this "try". Format is {"nth-try": dataset} """ if few_shot_eval is False: assert few_shot_k_or_percent is None assert few_shot_n_tries == 1 return {0: train_dataset} assert few_shot_k_or_percent is not None train_labels = get_labels(train_dataset) class_indices_mapping = create_class_indices_mapping(train_labels) train_dataset_dict: dict[int, Any] = {} is_percent = few_shot_k_or_percent < 1 if not is_percent: few_shot_k_or_percent = int(few_shot_k_or_percent) for t in range(few_shot_n_tries): t_subset_bool = _subset_dataset_per_class( class_indices_mapping=class_indices_mapping, n_or_percent_per_class=few_shot_k_or_percent, dataset_size=len(train_labels), is_percent=is_percent, seed=t, ) if len(train_labels.shape) > 1 and is_percent: t_subset_bool = _multilabel_rebalance_subset( class_indices_mapping=class_indices_mapping, n_or_percent_per_class=few_shot_k_or_percent, dataset_size=len(train_labels), labels=train_labels, indices_bool=t_subset_bool, seed=t, ) train_dataset_dict[t] = Subset(train_dataset, t_subset_bool.nonzero().flatten()) return train_dataset_dict def extract_features_for_dataset_dict( model, dataset_dict: dict[int, dict[int, Any]], batch_size: int, num_workers: int, gather_on_cpu=False, avgpool=False, ) -> dict[int, dict[str, torch.Tensor]]: """ Extract features for each subset of dataset in the context of few-shot evaluations """ few_shot_data_dict: dict[int, dict[str, torch.Tensor]] = {} for try_n, dataset in dataset_dict.items(): features, labels = extract_features_cell_dino( model, dataset, batch_size, num_workers, gather_on_cpu=gather_on_cpu, avgpool=avgpool ) few_shot_data_dict[try_n] = {"train_features": features, "train_labels": labels} return few_shot_data_dict def pad_multilabel_and_collate(batch, pad_value=-1): """ This method pads and collates a batch of (image, (index, target)) tuples, coming from DatasetWithEnumeratedTargets, with targets that are list of potentially varying sizes. The targets are padded to the length of the longest target list in the batch. """ maxlen = max(len(targets) for _, (_, targets) in batch) padded_batch = [ (image, (index, np.pad(targets, (0, maxlen - len(targets)), constant_values=pad_value))) for image, (index, targets) in batch ] return torch.utils.data.default_collate(padded_batch) class KnnModule(torch.nn.Module): """ Gets knn of test features from all processes on a chunk of the train features Each rank gets a chunk of the train features as well as a chunk of the test features. In `compute_neighbors`, for each rank one after the other, its chunk of test features is sent to all devices, partial knns are computed with each chunk of train features then collated back on the original device. """ def __init__(self, train_features, train_labels, nb_knn, T, device, num_classes=1000): super().__init__() self.global_rank = distributed.get_global_rank() self.global_size = distributed.get_global_size() self.device = device self.train_features_rank_T = train_features.chunk(self.global_size)[self.global_rank].T.to(self.device) # Labels can either be integers, or in a one-hot format self.candidates = train_labels.chunk(self.global_size)[self.global_rank].unsqueeze(0).to(self.device) self.nb_knn = nb_knn self.max_k = max(self.nb_knn) self.T = T self.num_classes = num_classes def _get_knn_sims_and_labels(self, similarity, train_labels): topk_sims, indices = similarity.topk(self.max_k, largest=True, sorted=True) if len(train_labels.shape) == 3: # If the labels are in one_hot format indices = indices.unsqueeze(2).expand(-1, -1, self.num_classes) # Orignally [bs, max_k] neighbors_labels = torch.gather(train_labels, 1, indices) return topk_sims, neighbors_labels def _similarity_for_rank(self, features_rank, source_rank): # Send the features from `source_rank` to all ranks broadcast_shape = torch.tensor(features_rank.shape).to(self.device) torch.distributed.broadcast(broadcast_shape, source_rank) broadcasted = features_rank if self.global_rank != source_rank: broadcasted = torch.zeros(*broadcast_shape, dtype=features_rank.dtype, device=self.device) torch.distributed.broadcast(broadcasted, source_rank) # Compute the neighbors for `source_rank` among `train_features_rank_T` similarity_rank = torch.mm(broadcasted, self.train_features_rank_T) candidate_labels = self.candidates.expand(len(similarity_rank), *self.candidates.shape[1:]) return self._get_knn_sims_and_labels(similarity_rank, candidate_labels) def _gather_all_knn_for_rank(self, topk_sims, neighbors_labels, target_rank): # Gather all neighbors for `target_rank` topk_sims_rank = retrieved_rank = None if self.global_rank == target_rank: topk_sims_rank = [torch.zeros_like(topk_sims) for _ in range(self.global_size)] retrieved_rank = [torch.zeros_like(neighbors_labels) for _ in range(self.global_size)] torch.distributed.gather(topk_sims, topk_sims_rank, dst=target_rank) torch.distributed.gather(neighbors_labels, retrieved_rank, dst=target_rank) if self.global_rank == target_rank: # Perform a second top-k on the k * global_size retrieved neighbors topk_sims_rank = torch.cat(topk_sims_rank, dim=1) retrieved_rank = torch.cat(retrieved_rank, dim=1) results = self._get_knn_sims_and_labels(topk_sims_rank, retrieved_rank) return results return None def compute_neighbors(self, features_rank): for rank in range(self.global_size): topk_sims, neighbors_labels = self._similarity_for_rank(features_rank, rank) results = self._gather_all_knn_for_rank(topk_sims, neighbors_labels, rank) if results is not None: topk_sims_rank, neighbors_labels_rank = results return topk_sims_rank, neighbors_labels_rank def forward(self, features_rank): """ Compute the results on all values of `self.nb_knn` neighbors from the full `self.max_k` """ assert all(k <= self.max_k for k in self.nb_knn) topk_sims, neighbors_labels = self.compute_neighbors(features_rank) batch_size = neighbors_labels.shape[0] topk_sims_transform = softmax(topk_sims / self.T, 1) voting_coefficient = topk_sims_transform.view(batch_size, -1, 1) if len(neighbors_labels.shape) == 2: # If the labels are not yet one hot neighbors_labels = one_hot(neighbors_labels, num_classes=self.num_classes) matmul = torch.mul(neighbors_labels, voting_coefficient) probas_for_k = {k: torch.sum(matmul[:, :k, :], 1) for k in self.nb_knn} return probas_for_k ================================================ FILE: dinov2/eval/depth/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. ================================================ FILE: dinov2/eval/depth/models/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .backbones import * # noqa: F403 from .builder import BACKBONES, DEPTHER, HEADS, LOSSES, build_backbone, build_depther, build_head, build_loss from .decode_heads import * # noqa: F403 from .depther import * # noqa: F403 from .losses import * # noqa: F403 ================================================ FILE: dinov2/eval/depth/models/backbones/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .vision_transformer import DinoVisionTransformer ================================================ FILE: dinov2/eval/depth/models/backbones/vision_transformer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from mmcv.runner import BaseModule from ..builder import BACKBONES @BACKBONES.register_module() class DinoVisionTransformer(BaseModule): """Vision Transformer.""" def __init__(self, *args, **kwargs): super().__init__() ================================================ FILE: dinov2/eval/depth/models/builder.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import warnings from mmcv.cnn import MODELS as MMCV_MODELS from mmcv.cnn.bricks.registry import ATTENTION as MMCV_ATTENTION from mmcv.utils import Registry MODELS = Registry("models", parent=MMCV_MODELS) ATTENTION = Registry("attention", parent=MMCV_ATTENTION) BACKBONES = MODELS NECKS = MODELS HEADS = MODELS LOSSES = MODELS DEPTHER = MODELS def build_backbone(cfg): """Build backbone.""" return BACKBONES.build(cfg) def build_neck(cfg): """Build neck.""" return NECKS.build(cfg) def build_head(cfg): """Build head.""" return HEADS.build(cfg) def build_loss(cfg): """Build loss.""" return LOSSES.build(cfg) def build_depther(cfg, train_cfg=None, test_cfg=None): """Build depther.""" if train_cfg is not None or test_cfg is not None: warnings.warn("train_cfg and test_cfg is deprecated, " "please specify them in model", UserWarning) assert cfg.get("train_cfg") is None or train_cfg is None, "train_cfg specified in both outer field and model field " assert cfg.get("test_cfg") is None or test_cfg is None, "test_cfg specified in both outer field and model field " return DEPTHER.build(cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) ================================================ FILE: dinov2/eval/depth/models/decode_heads/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .dpt_head import DPTHead from .linear_head import BNHead ================================================ FILE: dinov2/eval/depth/models/decode_heads/decode_head.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import copy from abc import ABCMeta, abstractmethod import mmcv import numpy as np import torch import torch.nn as nn from mmcv.runner import BaseModule, auto_fp16, force_fp32 from ...ops import resize from ..builder import build_loss class DepthBaseDecodeHead(BaseModule, metaclass=ABCMeta): """Base class for BaseDecodeHead. Args: in_channels (List): Input channels. channels (int): Channels after modules, before conv_depth. conv_cfg (dict|None): Config of conv layers. Default: None. act_cfg (dict): Config of activation layers. Default: dict(type='ReLU') loss_decode (dict): Config of decode loss. Default: dict(type='SigLoss'). sampler (dict|None): The config of depth map sampler. Default: None. align_corners (bool): align_corners argument of F.interpolate. Default: False. min_depth (int): Min depth in dataset setting. Default: 1e-3. max_depth (int): Max depth in dataset setting. Default: None. norm_cfg (dict|None): Config of norm layers. Default: None. classify (bool): Whether predict depth in a cls.-reg. manner. Default: False. n_bins (int): The number of bins used in cls. step. Default: 256. bins_strategy (str): The discrete strategy used in cls. step. Default: 'UD'. norm_strategy (str): The norm strategy on cls. probability distribution. Default: 'linear' scale_up (str): Whether predict depth in a scale-up manner. Default: False. """ def __init__( self, in_channels, channels=96, conv_cfg=None, act_cfg=dict(type="ReLU"), loss_decode=dict(type="SigLoss", valid_mask=True, loss_weight=10), sampler=None, align_corners=False, min_depth=1e-3, max_depth=None, norm_cfg=None, classify=False, n_bins=256, bins_strategy="UD", norm_strategy="linear", scale_up=False, ): super(DepthBaseDecodeHead, self).__init__() self.in_channels = in_channels self.channels = channels self.conv_cfg = conv_cfg self.act_cfg = act_cfg if isinstance(loss_decode, dict): self.loss_decode = build_loss(loss_decode) elif isinstance(loss_decode, (list, tuple)): self.loss_decode = nn.ModuleList() for loss in loss_decode: self.loss_decode.append(build_loss(loss)) self.align_corners = align_corners self.min_depth = min_depth self.max_depth = max_depth self.norm_cfg = norm_cfg self.classify = classify self.n_bins = n_bins self.scale_up = scale_up if self.classify: assert bins_strategy in ["UD", "SID"], "Support bins_strategy: UD, SID" assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid" self.bins_strategy = bins_strategy self.norm_strategy = norm_strategy self.softmax = nn.Softmax(dim=1) self.conv_depth = nn.Conv2d(channels, n_bins, kernel_size=3, padding=1, stride=1) else: self.conv_depth = nn.Conv2d(channels, 1, kernel_size=3, padding=1, stride=1) self.fp16_enabled = False self.relu = nn.ReLU() self.sigmoid = nn.Sigmoid() def extra_repr(self): """Extra repr.""" s = f"align_corners={self.align_corners}" return s @auto_fp16() @abstractmethod def forward(self, inputs, img_metas): """Placeholder of forward function.""" pass def forward_train(self, img, inputs, img_metas, depth_gt, train_cfg): """Forward function for training. Args: inputs (list[Tensor]): List of multi-level img features. img_metas (list[dict]): List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `depth/datasets/pipelines/formatting.py:Collect`. depth_gt (Tensor): GT depth train_cfg (dict): The training config. Returns: dict[str, Tensor]: a dictionary of loss components """ depth_pred = self.forward(inputs, img_metas) losses = self.losses(depth_pred, depth_gt) log_imgs = self.log_images(img[0], depth_pred[0], depth_gt[0], img_metas[0]) losses.update(**log_imgs) return losses def forward_test(self, inputs, img_metas, test_cfg): """Forward function for testing. Args: inputs (list[Tensor]): List of multi-level img features. img_metas (list[dict]): List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `depth/datasets/pipelines/formatting.py:Collect`. test_cfg (dict): The testing config. Returns: Tensor: Output depth map. """ return self.forward(inputs, img_metas) def depth_pred(self, feat): """Prediction each pixel.""" if self.classify: logit = self.conv_depth(feat) if self.bins_strategy == "UD": bins = torch.linspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device) elif self.bins_strategy == "SID": bins = torch.logspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device) # following Adabins, default linear if self.norm_strategy == "linear": logit = torch.relu(logit) eps = 0.1 logit = logit + eps logit = logit / logit.sum(dim=1, keepdim=True) elif self.norm_strategy == "softmax": logit = torch.softmax(logit, dim=1) elif self.norm_strategy == "sigmoid": logit = torch.sigmoid(logit) logit = logit / logit.sum(dim=1, keepdim=True) output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1) else: if self.scale_up: output = self.sigmoid(self.conv_depth(feat)) * self.max_depth else: output = self.relu(self.conv_depth(feat)) + self.min_depth return output @force_fp32(apply_to=("depth_pred",)) def losses(self, depth_pred, depth_gt): """Compute depth loss.""" loss = dict() depth_pred = resize( input=depth_pred, size=depth_gt.shape[2:], mode="bilinear", align_corners=self.align_corners, warning=False ) if not isinstance(self.loss_decode, nn.ModuleList): losses_decode = [self.loss_decode] else: losses_decode = self.loss_decode for loss_decode in losses_decode: if loss_decode.loss_name not in loss: loss[loss_decode.loss_name] = loss_decode(depth_pred, depth_gt) else: loss[loss_decode.loss_name] += loss_decode(depth_pred, depth_gt) return loss def log_images(self, img_path, depth_pred, depth_gt, img_meta): show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0)) show_img = show_img.numpy().astype(np.float32) show_img = mmcv.imdenormalize( show_img, img_meta["img_norm_cfg"]["mean"], img_meta["img_norm_cfg"]["std"], img_meta["img_norm_cfg"]["to_rgb"], ) show_img = np.clip(show_img, 0, 255) show_img = show_img.astype(np.uint8) show_img = show_img[:, :, ::-1] show_img = show_img.transpose(0, 2, 1) show_img = show_img.transpose(1, 0, 2) depth_pred = depth_pred / torch.max(depth_pred) depth_gt = depth_gt / torch.max(depth_gt) depth_pred_color = copy.deepcopy(depth_pred.detach().cpu()) depth_gt_color = copy.deepcopy(depth_gt.detach().cpu()) return {"img_rgb": show_img, "img_depth_pred": depth_pred_color, "img_depth_gt": depth_gt_color} ================================================ FILE: dinov2/eval/depth/models/decode_heads/dpt_head.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import math import torch import torch.nn as nn from mmcv.cnn import ConvModule, Linear, build_activation_layer from mmcv.runner import BaseModule from ...ops import resize from ..builder import HEADS from .decode_head import DepthBaseDecodeHead class Interpolate(nn.Module): def __init__(self, scale_factor, mode, align_corners=False): super(Interpolate, self).__init__() self.interp = nn.functional.interpolate self.scale_factor = scale_factor self.mode = mode self.align_corners = align_corners def forward(self, x): x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners) return x class HeadDepth(nn.Module): def __init__(self, features): super(HeadDepth, self).__init__() self.head = nn.Sequential( nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), Interpolate(scale_factor=2, mode="bilinear", align_corners=True), nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), ) def forward(self, x): x = self.head(x) return x class ReassembleBlocks(BaseModule): """ViTPostProcessBlock, process cls_token in ViT backbone output and rearrange the feature vector to feature map. Args: in_channels (int): ViT feature channels. Default: 768. out_channels (List): output channels of each stage. Default: [96, 192, 384, 768]. readout_type (str): Type of readout operation. Default: 'ignore'. patch_size (int): The patch size. Default: 16. init_cfg (dict, optional): Initialization config dict. Default: None. """ def __init__( self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16, init_cfg=None ): super(ReassembleBlocks, self).__init__(init_cfg) assert readout_type in ["ignore", "add", "project"] self.readout_type = readout_type self.patch_size = patch_size self.projects = nn.ModuleList( [ ConvModule( in_channels=in_channels, out_channels=out_channel, kernel_size=1, act_cfg=None, ) for out_channel in out_channels ] ) self.resize_layers = nn.ModuleList( [ nn.ConvTranspose2d( in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0 ), nn.ConvTranspose2d( in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0 ), nn.Identity(), nn.Conv2d( in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1 ), ] ) if self.readout_type == "project": self.readout_projects = nn.ModuleList() for _ in range(len(self.projects)): self.readout_projects.append( nn.Sequential(Linear(2 * in_channels, in_channels), build_activation_layer(dict(type="GELU"))) ) def forward(self, inputs): assert isinstance(inputs, list) out = [] for i, x in enumerate(inputs): assert len(x) == 2 x, cls_token = x[0], x[1] feature_shape = x.shape if self.readout_type == "project": x = x.flatten(2).permute((0, 2, 1)) readout = cls_token.unsqueeze(1).expand_as(x) x = self.readout_projects[i](torch.cat((x, readout), -1)) x = x.permute(0, 2, 1).reshape(feature_shape) elif self.readout_type == "add": x = x.flatten(2) + cls_token.unsqueeze(-1) x = x.reshape(feature_shape) else: pass x = self.projects[i](x) x = self.resize_layers[i](x) out.append(x) return out class PreActResidualConvUnit(BaseModule): """ResidualConvUnit, pre-activate residual unit. Args: in_channels (int): number of channels in the input feature map. act_cfg (dict): dictionary to construct and config activation layer. norm_cfg (dict): dictionary to construct and config norm layer. stride (int): stride of the first block. Default: 1 dilation (int): dilation rate for convs layers. Default: 1. init_cfg (dict, optional): Initialization config dict. Default: None. """ def __init__(self, in_channels, act_cfg, norm_cfg, stride=1, dilation=1, init_cfg=None): super(PreActResidualConvUnit, self).__init__(init_cfg) self.conv1 = ConvModule( in_channels, in_channels, 3, stride=stride, padding=dilation, dilation=dilation, norm_cfg=norm_cfg, act_cfg=act_cfg, bias=False, order=("act", "conv", "norm"), ) self.conv2 = ConvModule( in_channels, in_channels, 3, padding=1, norm_cfg=norm_cfg, act_cfg=act_cfg, bias=False, order=("act", "conv", "norm"), ) def forward(self, inputs): inputs_ = inputs.clone() x = self.conv1(inputs) x = self.conv2(x) return x + inputs_ class FeatureFusionBlock(BaseModule): """FeatureFusionBlock, merge feature map from different stages. Args: in_channels (int): Input channels. act_cfg (dict): The activation config for ResidualConvUnit. norm_cfg (dict): Config dict for normalization layer. expand (bool): Whether expand the channels in post process block. Default: False. align_corners (bool): align_corner setting for bilinear upsample. Default: True. init_cfg (dict, optional): Initialization config dict. Default: None. """ def __init__(self, in_channels, act_cfg, norm_cfg, expand=False, align_corners=True, init_cfg=None): super(FeatureFusionBlock, self).__init__(init_cfg) self.in_channels = in_channels self.expand = expand self.align_corners = align_corners self.out_channels = in_channels if self.expand: self.out_channels = in_channels // 2 self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_cfg=None, bias=True) self.res_conv_unit1 = PreActResidualConvUnit(in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg) self.res_conv_unit2 = PreActResidualConvUnit(in_channels=self.in_channels, act_cfg=act_cfg, norm_cfg=norm_cfg) def forward(self, *inputs): x = inputs[0] if len(inputs) == 2: if x.shape != inputs[1].shape: res = resize(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False) else: res = inputs[1] x = x + self.res_conv_unit1(res) x = self.res_conv_unit2(x) x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners) x = self.project(x) return x @HEADS.register_module() class DPTHead(DepthBaseDecodeHead): """Vision Transformers for Dense Prediction. This head is implemented of `DPT `_. Args: embed_dims (int): The embed dimension of the ViT backbone. Default: 768. post_process_channels (List): Out channels of post process conv layers. Default: [96, 192, 384, 768]. readout_type (str): Type of readout operation. Default: 'ignore'. patch_size (int): The patch size. Default: 16. expand_channels (bool): Whether expand the channels in post process block. Default: False. """ def __init__( self, embed_dims=768, post_process_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16, expand_channels=False, **kwargs ): super(DPTHead, self).__init__(**kwargs) self.in_channels = self.in_channels self.expand_channels = expand_channels self.reassemble_blocks = ReassembleBlocks(embed_dims, post_process_channels, readout_type, patch_size) self.post_process_channels = [ channel * math.pow(2, i) if expand_channels else channel for i, channel in enumerate(post_process_channels) ] self.convs = nn.ModuleList() for channel in self.post_process_channels: self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_cfg=None, bias=False)) self.fusion_blocks = nn.ModuleList() for _ in range(len(self.convs)): self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_cfg, self.norm_cfg)) self.fusion_blocks[0].res_conv_unit1 = None self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_cfg=self.norm_cfg) self.num_fusion_blocks = len(self.fusion_blocks) self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers) self.num_post_process_channels = len(self.post_process_channels) assert self.num_fusion_blocks == self.num_reassemble_blocks assert self.num_reassemble_blocks == self.num_post_process_channels self.conv_depth = HeadDepth(self.channels) def forward(self, inputs, img_metas): assert len(inputs) == self.num_reassemble_blocks x = [inp for inp in inputs] x = self.reassemble_blocks(x) x = [self.convs[i](feature) for i, feature in enumerate(x)] out = self.fusion_blocks[0](x[-1]) for i in range(1, len(self.fusion_blocks)): out = self.fusion_blocks[i](out, x[-(i + 1)]) out = self.project(out) out = self.depth_pred(out) return out ================================================ FILE: dinov2/eval/depth/models/decode_heads/linear_head.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn from ...ops import resize from ..builder import HEADS from .decode_head import DepthBaseDecodeHead @HEADS.register_module() class BNHead(DepthBaseDecodeHead): """Just a batchnorm.""" def __init__(self, input_transform="resize_concat", in_index=(0, 1, 2, 3), upsample=1, **kwargs): super().__init__(**kwargs) self.input_transform = input_transform self.in_index = in_index self.upsample = upsample # self.bn = nn.SyncBatchNorm(self.in_channels) if self.classify: self.conv_depth = nn.Conv2d(self.channels, self.n_bins, kernel_size=1, padding=0, stride=1) else: self.conv_depth = nn.Conv2d(self.channels, 1, kernel_size=1, padding=0, stride=1) def _transform_inputs(self, inputs): """Transform inputs for decoder. Args: inputs (list[Tensor]): List of multi-level img features. Returns: Tensor: The transformed inputs """ if "concat" in self.input_transform: inputs = [inputs[i] for i in self.in_index] if "resize" in self.input_transform: inputs = [ resize( input=x, size=[s * self.upsample for s in inputs[0].shape[2:]], mode="bilinear", align_corners=self.align_corners, ) for x in inputs ] inputs = torch.cat(inputs, dim=1) elif self.input_transform == "multiple_select": inputs = [inputs[i] for i in self.in_index] else: inputs = inputs[self.in_index] return inputs def _forward_feature(self, inputs, img_metas=None, **kwargs): """Forward function for feature maps before classifying each pixel with ``self.cls_seg`` fc. Args: inputs (list[Tensor]): List of multi-level img features. Returns: feats (Tensor): A tensor of shape (batch_size, self.channels, H, W) which is feature map for last layer of decoder head. """ # accept lists (for cls token) inputs = list(inputs) for i, x in enumerate(inputs): if len(x) == 2: x, cls_token = x[0], x[1] if len(x.shape) == 2: x = x[:, :, None, None] cls_token = cls_token[:, :, None, None].expand_as(x) inputs[i] = torch.cat((x, cls_token), 1) else: x = x[0] if len(x.shape) == 2: x = x[:, :, None, None] inputs[i] = x x = self._transform_inputs(inputs) # feats = self.bn(x) return x def forward(self, inputs, img_metas=None, **kwargs): """Forward function.""" output = self._forward_feature(inputs, img_metas=img_metas, **kwargs) output = self.depth_pred(output) return output ================================================ FILE: dinov2/eval/depth/models/depther/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .base import BaseDepther from .encoder_decoder import DepthEncoderDecoder ================================================ FILE: dinov2/eval/depth/models/depther/base.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from abc import ABCMeta, abstractmethod from collections import OrderedDict import torch import torch.distributed as dist from mmcv.runner import BaseModule, auto_fp16 class BaseDepther(BaseModule, metaclass=ABCMeta): """Base class for depther.""" def __init__(self, init_cfg=None): super(BaseDepther, self).__init__(init_cfg) self.fp16_enabled = False @property def with_neck(self): """bool: whether the depther has neck""" return hasattr(self, "neck") and self.neck is not None @property def with_auxiliary_head(self): """bool: whether the depther has auxiliary head""" return hasattr(self, "auxiliary_head") and self.auxiliary_head is not None @property def with_decode_head(self): """bool: whether the depther has decode head""" return hasattr(self, "decode_head") and self.decode_head is not None @abstractmethod def extract_feat(self, imgs): """Placeholder for extract features from images.""" pass @abstractmethod def encode_decode(self, img, img_metas): """Placeholder for encode images with backbone and decode into a semantic depth map of the same size as input.""" pass @abstractmethod def forward_train(self, imgs, img_metas, **kwargs): """Placeholder for Forward function for training.""" pass @abstractmethod def simple_test(self, img, img_meta, **kwargs): """Placeholder for single image test.""" pass @abstractmethod def aug_test(self, imgs, img_metas, **kwargs): """Placeholder for augmentation test.""" pass def forward_test(self, imgs, img_metas, **kwargs): """ Args: imgs (List[Tensor]): the outer list indicates test-time augmentations and inner Tensor should have a shape NxCxHxW, which contains all images in the batch. img_metas (List[List[dict]]): the outer list indicates test-time augs (multiscale, flip, etc.) and the inner list indicates images in a batch. """ for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]: if not isinstance(var, list): raise TypeError(f"{name} must be a list, but got " f"{type(var)}") num_augs = len(imgs) if num_augs != len(img_metas): raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})") # all images in the same aug batch all of the same ori_shape and pad # shape for img_meta in img_metas: ori_shapes = [_["ori_shape"] for _ in img_meta] assert all(shape == ori_shapes[0] for shape in ori_shapes) img_shapes = [_["img_shape"] for _ in img_meta] assert all(shape == img_shapes[0] for shape in img_shapes) pad_shapes = [_["pad_shape"] for _ in img_meta] assert all(shape == pad_shapes[0] for shape in pad_shapes) if num_augs == 1: return self.simple_test(imgs[0], img_metas[0], **kwargs) else: return self.aug_test(imgs, img_metas, **kwargs) @auto_fp16(apply_to=("img",)) def forward(self, img, img_metas, return_loss=True, **kwargs): """Calls either :func:`forward_train` or :func:`forward_test` depending on whether ``return_loss`` is ``True``. Note this setting will change the expected inputs. When ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor and List[dict]), and when ``resturn_loss=False``, img and img_meta should be double nested (i.e. List[Tensor], List[List[dict]]), with the outer list indicating test time augmentations. """ if return_loss: return self.forward_train(img, img_metas, **kwargs) else: return self.forward_test(img, img_metas, **kwargs) def train_step(self, data_batch, optimizer, **kwargs): """The iteration step during training. This method defines an iteration step during training, except for the back propagation and optimizer updating, which are done in an optimizer hook. Note that in some complicated cases or models, the whole process including back propagation and optimizer updating is also defined in this method, such as GAN. Args: data (dict): The output of dataloader. optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of runner is passed to ``train_step()``. This argument is unused and reserved. Returns: dict: It should contain at least 3 keys: ``loss``, ``log_vars``, ``num_samples``. ``loss`` is a tensor for back propagation, which can be a weighted sum of multiple losses. ``log_vars`` contains all the variables to be sent to the logger. ``num_samples`` indicates the batch size (when the model is DDP, it means the batch size on each GPU), which is used for averaging the logs. """ losses = self(**data_batch) # split losses and images real_losses = {} log_imgs = {} for k, v in losses.items(): if "img" in k: log_imgs[k] = v else: real_losses[k] = v loss, log_vars = self._parse_losses(real_losses) outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs) return outputs def val_step(self, data_batch, **kwargs): """The iteration step during validation. This method shares the same signature as :func:`train_step`, but used during val epochs. Note that the evaluation after training epochs is not implemented with this method, but an evaluation hook. """ output = self(**data_batch, **kwargs) return output @staticmethod def _parse_losses(losses): """Parse the raw outputs (losses) of the network. Args: losses (dict): Raw output of the network, which usually contain losses and other necessary information. Returns: tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor which may be a weighted sum of all losses, log_vars contains all the variables to be sent to the logger. """ log_vars = OrderedDict() for loss_name, loss_value in losses.items(): if isinstance(loss_value, torch.Tensor): log_vars[loss_name] = loss_value.mean() elif isinstance(loss_value, list): log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) else: raise TypeError(f"{loss_name} is not a tensor or list of tensors") loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key) log_vars["loss"] = loss for loss_name, loss_value in log_vars.items(): # reduce loss when distributed training if dist.is_available() and dist.is_initialized(): loss_value = loss_value.data.clone() dist.all_reduce(loss_value.div_(dist.get_world_size())) log_vars[loss_name] = loss_value.item() return loss, log_vars ================================================ FILE: dinov2/eval/depth/models/depther/encoder_decoder.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn.functional as F from ...models import builder from ...models.builder import DEPTHER from ...ops import resize from .base import BaseDepther def add_prefix(inputs, prefix): """Add prefix for dict. Args: inputs (dict): The input dict with str keys. prefix (str): The prefix to add. Returns: dict: The dict with keys updated with ``prefix``. """ outputs = dict() for name, value in inputs.items(): outputs[f"{prefix}.{name}"] = value return outputs @DEPTHER.register_module() class DepthEncoderDecoder(BaseDepther): """Encoder Decoder depther. EncoderDecoder typically consists of backbone, (neck) and decode_head. """ def __init__(self, backbone, decode_head, neck=None, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None): super(DepthEncoderDecoder, self).__init__(init_cfg) if pretrained is not None: assert backbone.get("pretrained") is None, "both backbone and depther set pretrained weight" backbone.pretrained = pretrained self.backbone = builder.build_backbone(backbone) self._init_decode_head(decode_head) if neck is not None: self.neck = builder.build_neck(neck) self.train_cfg = train_cfg self.test_cfg = test_cfg assert self.with_decode_head def _init_decode_head(self, decode_head): """Initialize ``decode_head``""" self.decode_head = builder.build_head(decode_head) self.align_corners = self.decode_head.align_corners def extract_feat(self, img): """Extract features from images.""" x = self.backbone(img) if self.with_neck: x = self.neck(x) return x def encode_decode(self, img, img_metas, rescale=True, size=None): """Encode images with backbone and decode into a depth estimation map of the same size as input.""" x = self.extract_feat(img) out = self._decode_head_forward_test(x, img_metas) # crop the pred depth to the certain range. out = torch.clamp(out, min=self.decode_head.min_depth, max=self.decode_head.max_depth) if rescale: if size is None: if img_metas is not None: size = img_metas[0]["ori_shape"][:2] else: size = img.shape[2:] out = resize(input=out, size=size, mode="bilinear", align_corners=self.align_corners) return out def _decode_head_forward_train(self, img, x, img_metas, depth_gt, **kwargs): """Run forward function and calculate loss for decode head in training.""" losses = dict() loss_decode = self.decode_head.forward_train(img, x, img_metas, depth_gt, self.train_cfg, **kwargs) losses.update(add_prefix(loss_decode, "decode")) return losses def _decode_head_forward_test(self, x, img_metas): """Run forward function and calculate loss for decode head in inference.""" depth_pred = self.decode_head.forward_test(x, img_metas, self.test_cfg) return depth_pred def forward_dummy(self, img): """Dummy forward function.""" depth = self.encode_decode(img, None) return depth def forward_train(self, img, img_metas, depth_gt, **kwargs): """Forward function for training. Args: img (Tensor): Input images. img_metas (list[dict]): List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `depth/datasets/pipelines/formatting.py:Collect`. depth_gt (Tensor): Depth gt used if the architecture supports depth estimation task. Returns: dict[str, Tensor]: a dictionary of loss components """ x = self.extract_feat(img) losses = dict() # the last of x saves the info from neck loss_decode = self._decode_head_forward_train(img, x, img_metas, depth_gt, **kwargs) losses.update(loss_decode) return losses def whole_inference(self, img, img_meta, rescale, size=None): """Inference with full image.""" depth_pred = self.encode_decode(img, img_meta, rescale, size=size) return depth_pred def slide_inference(self, img, img_meta, rescale): """Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding. """ h_stride, w_stride = self.test_cfg.stride h_crop, w_crop = self.test_cfg.crop_size batch_size, _, h_img, w_img = img.size() h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1 w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1 preds = img.new_zeros((batch_size, 1, h_img, w_img)) count_mat = img.new_zeros((batch_size, 1, h_img, w_img)) for h_idx in range(h_grids): for w_idx in range(w_grids): y1 = h_idx * h_stride x1 = w_idx * w_stride y2 = min(y1 + h_crop, h_img) x2 = min(x1 + w_crop, w_img) y1 = max(y2 - h_crop, 0) x1 = max(x2 - w_crop, 0) crop_img = img[:, :, y1:y2, x1:x2] depth_pred = self.encode_decode(crop_img, img_meta, rescale) preds += F.pad(depth_pred, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2))) count_mat[:, :, y1:y2, x1:x2] += 1 assert (count_mat == 0).sum() == 0 if torch.onnx.is_in_onnx_export(): # cast count_mat to constant while exporting to ONNX count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device) preds = preds / count_mat return preds def inference(self, img, img_meta, rescale, size=None): """Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `depth/datasets/pipelines/formatting.py:Collect`. rescale (bool): Whether rescale back to original shape. Returns: Tensor: The output depth map. """ assert self.test_cfg.mode in ["slide", "whole"] ori_shape = img_meta[0]["ori_shape"] assert all(_["ori_shape"] == ori_shape for _ in img_meta) if self.test_cfg.mode == "slide": depth_pred = self.slide_inference(img, img_meta, rescale) else: depth_pred = self.whole_inference(img, img_meta, rescale, size=size) output = depth_pred flip = img_meta[0]["flip"] if flip: flip_direction = img_meta[0]["flip_direction"] assert flip_direction in ["horizontal", "vertical"] if flip_direction == "horizontal": output = output.flip(dims=(3,)) elif flip_direction == "vertical": output = output.flip(dims=(2,)) return output def simple_test(self, img, img_meta, rescale=True): """Simple test with single image.""" depth_pred = self.inference(img, img_meta, rescale) if torch.onnx.is_in_onnx_export(): # our inference backend only support 4D output depth_pred = depth_pred.unsqueeze(0) return depth_pred depth_pred = depth_pred.cpu().numpy() # unravel batch dim depth_pred = list(depth_pred) return depth_pred def aug_test(self, imgs, img_metas, rescale=True): """Test with augmentations. Only rescale=True is supported. """ # aug_test rescale all imgs back to ori_shape for now assert rescale # to save memory, we get augmented depth logit inplace depth_pred = self.inference(imgs[0], img_metas[0], rescale) for i in range(1, len(imgs)): cur_depth_pred = self.inference(imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:]) depth_pred += cur_depth_pred depth_pred /= len(imgs) depth_pred = depth_pred.cpu().numpy() # unravel batch dim depth_pred = list(depth_pred) return depth_pred ================================================ FILE: dinov2/eval/depth/models/losses/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .gradientloss import GradientLoss from .sigloss import SigLoss ================================================ FILE: dinov2/eval/depth/models/losses/gradientloss.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn from ...models.builder import LOSSES @LOSSES.register_module() class GradientLoss(nn.Module): """GradientLoss. Adapted from https://www.cs.cornell.edu/projects/megadepth/ Args: valid_mask (bool): Whether filter invalid gt (gt > 0). Default: True. loss_weight (float): Weight of the loss. Default: 1.0. max_depth (int): When filtering invalid gt, set a max threshold. Default: None. """ def __init__(self, valid_mask=True, loss_weight=1.0, max_depth=None, loss_name="loss_grad"): super(GradientLoss, self).__init__() self.valid_mask = valid_mask self.loss_weight = loss_weight self.max_depth = max_depth self.loss_name = loss_name self.eps = 0.001 # avoid grad explode def gradientloss(self, input, target): input_downscaled = [input] + [input[:: 2 * i, :: 2 * i] for i in range(1, 4)] target_downscaled = [target] + [target[:: 2 * i, :: 2 * i] for i in range(1, 4)] gradient_loss = 0 for input, target in zip(input_downscaled, target_downscaled): if self.valid_mask: mask = target > 0 if self.max_depth is not None: mask = torch.logical_and(target > 0, target <= self.max_depth) N = torch.sum(mask) else: mask = torch.ones_like(target) N = input.numel() input_log = torch.log(input + self.eps) target_log = torch.log(target + self.eps) log_d_diff = input_log - target_log log_d_diff = torch.mul(log_d_diff, mask) v_gradient = torch.abs(log_d_diff[0:-2, :] - log_d_diff[2:, :]) v_mask = torch.mul(mask[0:-2, :], mask[2:, :]) v_gradient = torch.mul(v_gradient, v_mask) h_gradient = torch.abs(log_d_diff[:, 0:-2] - log_d_diff[:, 2:]) h_mask = torch.mul(mask[:, 0:-2], mask[:, 2:]) h_gradient = torch.mul(h_gradient, h_mask) gradient_loss += (torch.sum(h_gradient) + torch.sum(v_gradient)) / N return gradient_loss def forward(self, depth_pred, depth_gt): """Forward function.""" gradient_loss = self.loss_weight * self.gradientloss(depth_pred, depth_gt) return gradient_loss ================================================ FILE: dinov2/eval/depth/models/losses/sigloss.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn from ...models.builder import LOSSES @LOSSES.register_module() class SigLoss(nn.Module): """SigLoss. This follows `AdaBins `_. Args: valid_mask (bool): Whether filter invalid gt (gt > 0). Default: True. loss_weight (float): Weight of the loss. Default: 1.0. max_depth (int): When filtering invalid gt, set a max threshold. Default: None. warm_up (bool): A simple warm up stage to help convergence. Default: False. warm_iter (int): The number of warm up stage. Default: 100. """ def __init__( self, valid_mask=True, loss_weight=1.0, max_depth=None, warm_up=False, warm_iter=100, loss_name="sigloss" ): super(SigLoss, self).__init__() self.valid_mask = valid_mask self.loss_weight = loss_weight self.max_depth = max_depth self.loss_name = loss_name self.eps = 0.001 # avoid grad explode # HACK: a hack implementation for warmup sigloss self.warm_up = warm_up self.warm_iter = warm_iter self.warm_up_counter = 0 def sigloss(self, input, target): if self.valid_mask: valid_mask = target > 0 if self.max_depth is not None: valid_mask = torch.logical_and(target > 0, target <= self.max_depth) input = input[valid_mask] target = target[valid_mask] if self.warm_up: if self.warm_up_counter < self.warm_iter: g = torch.log(input + self.eps) - torch.log(target + self.eps) g = 0.15 * torch.pow(torch.mean(g), 2) self.warm_up_counter += 1 return torch.sqrt(g) g = torch.log(input + self.eps) - torch.log(target + self.eps) Dg = torch.var(g) + 0.15 * torch.pow(torch.mean(g), 2) return torch.sqrt(Dg) def forward(self, depth_pred, depth_gt): """Forward function.""" loss_depth = self.loss_weight * self.sigloss(depth_pred, depth_gt) return loss_depth ================================================ FILE: dinov2/eval/depth/ops/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .wrappers import resize ================================================ FILE: dinov2/eval/depth/ops/wrappers.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import warnings import torch.nn.functional as F def resize(input, size=None, scale_factor=None, mode="nearest", align_corners=None, warning=False): if warning: if size is not None and align_corners: input_h, input_w = tuple(int(x) for x in input.shape[2:]) output_h, output_w = tuple(int(x) for x in size) if output_h > input_h or output_w > output_h: if ( (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1) and (output_h - 1) % (input_h - 1) and (output_w - 1) % (input_w - 1) ): warnings.warn( f"When align_corners={align_corners}, " "the output would more aligned if " f"input size {(input_h, input_w)} is `x+1` and " f"out size {(output_h, output_w)} is `nx+1`" ) return F.interpolate(input, size, scale_factor, mode, align_corners) ================================================ FILE: dinov2/eval/knn.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import argparse from functools import partial import json import logging import os import sys from typing import List, Optional import torch from torch.nn.functional import one_hot, softmax import dinov2.distributed as distributed from dinov2.data import SamplerType, make_data_loader, make_dataset from dinov2.data.transforms import make_classification_eval_transform from dinov2.eval.metrics import AccuracyAveraging, build_topk_accuracy_metric from dinov2.eval.setup import get_args_parser as get_setup_args_parser from dinov2.eval.setup import setup_and_build_model from dinov2.eval.utils import ModelWithNormalize, evaluate, extract_features logger = logging.getLogger("dinov2") def get_args_parser( description: Optional[str] = None, parents: Optional[List[argparse.ArgumentParser]] = None, add_help: bool = True, ): parents = parents or [] setup_args_parser = get_setup_args_parser(parents=parents, add_help=False) parents = [setup_args_parser] parser = argparse.ArgumentParser( description=description, parents=parents, add_help=add_help, ) parser.add_argument( "--train-dataset", dest="train_dataset_str", type=str, help="Training dataset", ) parser.add_argument( "--val-dataset", dest="val_dataset_str", type=str, help="Validation dataset", ) parser.add_argument( "--nb_knn", nargs="+", type=int, help="Number of NN to use. 20 is usually working the best.", ) parser.add_argument( "--temperature", type=float, help="Temperature used in the voting coefficient", ) parser.add_argument( "--gather-on-cpu", action="store_true", help="Whether to gather the train features on cpu, slower" "but useful to avoid OOM for large datasets (e.g. ImageNet22k).", ) parser.add_argument( "--batch-size", type=int, help="Batch size.", ) parser.add_argument( "--n-per-class-list", nargs="+", type=int, help="Number to take per class", ) parser.add_argument( "--n-tries", type=int, help="Number of tries", ) parser.set_defaults( train_dataset_str="ImageNet:split=TRAIN", val_dataset_str="ImageNet:split=VAL", nb_knn=[10, 20, 100, 200], temperature=0.07, batch_size=256, n_per_class_list=[-1], n_tries=1, ) return parser class KnnModule(torch.nn.Module): """ Gets knn of test features from all processes on a chunk of the train features Each rank gets a chunk of the train features as well as a chunk of the test features. In `compute_neighbors`, for each rank one after the other, its chunk of test features is sent to all devices, partial knns are computed with each chunk of train features then collated back on the original device. """ def __init__(self, train_features, train_labels, nb_knn, T, device, num_classes=1000): super().__init__() self.global_rank = distributed.get_global_rank() self.global_size = distributed.get_global_size() self.device = device self.train_features_rank_T = train_features.chunk(self.global_size)[self.global_rank].T.to(self.device) self.candidates = train_labels.chunk(self.global_size)[self.global_rank].view(1, -1).to(self.device) self.nb_knn = nb_knn self.max_k = max(self.nb_knn) self.T = T self.num_classes = num_classes def _get_knn_sims_and_labels(self, similarity, train_labels): topk_sims, indices = similarity.topk(self.max_k, largest=True, sorted=True) neighbors_labels = torch.gather(train_labels, 1, indices) return topk_sims, neighbors_labels def _similarity_for_rank(self, features_rank, source_rank): # Send the features from `source_rank` to all ranks broadcast_shape = torch.tensor(features_rank.shape).to(self.device) torch.distributed.broadcast(broadcast_shape, source_rank) broadcasted = features_rank if self.global_rank != source_rank: broadcasted = torch.zeros(*broadcast_shape, dtype=features_rank.dtype, device=self.device) torch.distributed.broadcast(broadcasted, source_rank) # Compute the neighbors for `source_rank` among `train_features_rank_T` similarity_rank = torch.mm(broadcasted, self.train_features_rank_T) candidate_labels = self.candidates.expand(len(similarity_rank), -1) return self._get_knn_sims_and_labels(similarity_rank, candidate_labels) def _gather_all_knn_for_rank(self, topk_sims, neighbors_labels, target_rank): # Gather all neighbors for `target_rank` topk_sims_rank = retrieved_rank = None if self.global_rank == target_rank: topk_sims_rank = [torch.zeros_like(topk_sims) for _ in range(self.global_size)] retrieved_rank = [torch.zeros_like(neighbors_labels) for _ in range(self.global_size)] torch.distributed.gather(topk_sims, topk_sims_rank, dst=target_rank) torch.distributed.gather(neighbors_labels, retrieved_rank, dst=target_rank) if self.global_rank == target_rank: # Perform a second top-k on the k * global_size retrieved neighbors topk_sims_rank = torch.cat(topk_sims_rank, dim=1) retrieved_rank = torch.cat(retrieved_rank, dim=1) results = self._get_knn_sims_and_labels(topk_sims_rank, retrieved_rank) return results return None def compute_neighbors(self, features_rank): for rank in range(self.global_size): topk_sims, neighbors_labels = self._similarity_for_rank(features_rank, rank) results = self._gather_all_knn_for_rank(topk_sims, neighbors_labels, rank) if results is not None: topk_sims_rank, neighbors_labels_rank = results return topk_sims_rank, neighbors_labels_rank def forward(self, features_rank): """ Compute the results on all values of `self.nb_knn` neighbors from the full `self.max_k` """ assert all(k <= self.max_k for k in self.nb_knn) topk_sims, neighbors_labels = self.compute_neighbors(features_rank) batch_size = neighbors_labels.shape[0] topk_sims_transform = softmax(topk_sims / self.T, 1) matmul = torch.mul( one_hot(neighbors_labels, num_classes=self.num_classes), topk_sims_transform.view(batch_size, -1, 1), ) probas_for_k = {k: torch.sum(matmul[:, :k, :], 1) for k in self.nb_knn} return probas_for_k class DictKeysModule(torch.nn.Module): def __init__(self, keys): super().__init__() self.keys = keys def forward(self, features_dict, targets): for k in self.keys: features_dict = features_dict[k] return {"preds": features_dict, "target": targets} def create_module_dict(*, module, n_per_class_list, n_tries, nb_knn, train_features, train_labels): modules = {} mapping = create_class_indices_mapping(train_labels) for npc in n_per_class_list: if npc < 0: # Only one try needed when using the full data full_module = module( train_features=train_features, train_labels=train_labels, nb_knn=nb_knn, ) modules["full"] = ModuleDictWithForward({"1": full_module}) continue all_tries = {} for t in range(n_tries): final_indices = filter_train(mapping, npc, seed=t) k_list = list(set(nb_knn + [npc])) k_list = sorted([el for el in k_list if el <= npc]) all_tries[str(t)] = module( train_features=train_features[final_indices], train_labels=train_labels[final_indices], nb_knn=k_list, ) modules[f"{npc} per class"] = ModuleDictWithForward(all_tries) return ModuleDictWithForward(modules) def filter_train(mapping, n_per_class, seed): torch.manual_seed(seed) final_indices = [] for k in mapping.keys(): index = torch.randperm(len(mapping[k]))[:n_per_class] final_indices.append(mapping[k][index]) return torch.cat(final_indices).squeeze() def create_class_indices_mapping(labels): unique_labels, inverse = torch.unique(labels, return_inverse=True) mapping = {unique_labels[i]: (inverse == i).nonzero() for i in range(len(unique_labels))} return mapping class ModuleDictWithForward(torch.nn.ModuleDict): def forward(self, *args, **kwargs): return {k: module(*args, **kwargs) for k, module in self._modules.items()} def eval_knn( model, train_dataset, val_dataset, accuracy_averaging, nb_knn, temperature, batch_size, num_workers, gather_on_cpu, n_per_class_list=[-1], n_tries=1, ): model = ModelWithNormalize(model) logger.info("Extracting features for train set...") train_features, train_labels = extract_features( model, train_dataset, batch_size, num_workers, gather_on_cpu=gather_on_cpu ) logger.info(f"Train features created, shape {train_features.shape}.") val_dataloader = make_data_loader( dataset=val_dataset, batch_size=batch_size, num_workers=num_workers, sampler_type=SamplerType.DISTRIBUTED, drop_last=False, shuffle=False, persistent_workers=True, ) num_classes = train_labels.max() + 1 metric_collection = build_topk_accuracy_metric(accuracy_averaging, num_classes=num_classes) device = torch.cuda.current_device() partial_module = partial(KnnModule, T=temperature, device=device, num_classes=num_classes) knn_module_dict = create_module_dict( module=partial_module, n_per_class_list=n_per_class_list, n_tries=n_tries, nb_knn=nb_knn, train_features=train_features, train_labels=train_labels, ) postprocessors, metrics = {}, {} for n_per_class, knn_module in knn_module_dict.items(): for t, knn_try in knn_module.items(): postprocessors = { **postprocessors, **{(n_per_class, t, k): DictKeysModule([n_per_class, t, k]) for k in knn_try.nb_knn}, } metrics = {**metrics, **{(n_per_class, t, k): metric_collection.clone() for k in knn_try.nb_knn}} model_with_knn = torch.nn.Sequential(model, knn_module_dict) # ============ evaluation ... ============ logger.info("Start the k-NN classification.") _, results_dict = evaluate(model_with_knn, val_dataloader, postprocessors, metrics, device) # Averaging the results over the n tries for each value of n_per_class for n_per_class, knn_module in knn_module_dict.items(): first_try = list(knn_module.keys())[0] k_list = knn_module[first_try].nb_knn for k in k_list: keys = results_dict[(n_per_class, first_try, k)].keys() # keys are e.g. `top-1` and `top-5` results_dict[(n_per_class, k)] = { key: torch.mean(torch.stack([results_dict[(n_per_class, t, k)][key] for t in knn_module.keys()])) for key in keys } for t in knn_module.keys(): del results_dict[(n_per_class, t, k)] return results_dict def eval_knn_with_model( model, output_dir, train_dataset_str="ImageNet:split=TRAIN", val_dataset_str="ImageNet:split=VAL", nb_knn=(10, 20, 100, 200), temperature=0.07, autocast_dtype=torch.float, accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY, transform=None, gather_on_cpu=False, batch_size=256, num_workers=5, n_per_class_list=[-1], n_tries=1, ): transform = transform or make_classification_eval_transform() train_dataset = make_dataset( dataset_str=train_dataset_str, transform=transform, ) val_dataset = make_dataset( dataset_str=val_dataset_str, transform=transform, ) with torch.cuda.amp.autocast(dtype=autocast_dtype): results_dict_knn = eval_knn( model=model, train_dataset=train_dataset, val_dataset=val_dataset, accuracy_averaging=accuracy_averaging, nb_knn=nb_knn, temperature=temperature, batch_size=batch_size, num_workers=num_workers, gather_on_cpu=gather_on_cpu, n_per_class_list=n_per_class_list, n_tries=n_tries, ) results_dict = {} if distributed.is_main_process(): for knn_ in results_dict_knn.keys(): top1 = results_dict_knn[knn_]["top-1"].item() * 100.0 top5 = results_dict_knn[knn_]["top-5"].item() * 100.0 results_dict[f"{knn_} Top 1"] = top1 results_dict[f"{knn_} Top 5"] = top5 logger.info(f"{knn_} classifier result: Top1: {top1:.2f} Top5: {top5:.2f}") metrics_file_path = os.path.join(output_dir, "results_eval_knn.json") with open(metrics_file_path, "a") as f: for k, v in results_dict.items(): f.write(json.dumps({k: v}) + "\n") if distributed.is_enabled(): torch.distributed.barrier() return results_dict def main(args): model, autocast_dtype = setup_and_build_model(args) eval_knn_with_model( model=model, output_dir=args.output_dir, train_dataset_str=args.train_dataset_str, val_dataset_str=args.val_dataset_str, nb_knn=args.nb_knn, temperature=args.temperature, autocast_dtype=autocast_dtype, accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY, transform=None, gather_on_cpu=args.gather_on_cpu, batch_size=args.batch_size, num_workers=5, n_per_class_list=args.n_per_class_list, n_tries=args.n_tries, ) return 0 if __name__ == "__main__": description = "DINOv2 k-NN evaluation" args_parser = get_args_parser(description=description) args = args_parser.parse_args() sys.exit(main(args)) ================================================ FILE: dinov2/eval/linear.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import argparse from functools import partial import json import logging import os import sys from typing import List, Optional import numpy as np import torch import torch.nn as nn from torch.nn.parallel import DistributedDataParallel from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer from dinov2.data import SamplerType, make_data_loader, make_dataset from dinov2.data.transforms import make_classification_eval_transform, make_classification_train_transform import dinov2.distributed as distributed from dinov2.eval.metrics import MetricType, build_metric from dinov2.eval.setup import get_args_parser as get_setup_args_parser from dinov2.eval.setup import setup_and_build_model from dinov2.eval.utils import ModelWithIntermediateLayers, evaluate from dinov2.logging import MetricLogger logger = logging.getLogger("dinov2") def get_args_parser( description: Optional[str] = None, parents: Optional[List[argparse.ArgumentParser]] = None, add_help: bool = True, ): parents = parents or [] setup_args_parser = get_setup_args_parser(parents=parents, add_help=False) parents = [setup_args_parser] parser = argparse.ArgumentParser( description=description, parents=parents, add_help=add_help, ) parser.add_argument( "--train-dataset", dest="train_dataset_str", type=str, help="Training dataset", ) parser.add_argument( "--val-dataset", dest="val_dataset_str", type=str, help="Validation dataset", ) parser.add_argument( "--test-datasets", dest="test_dataset_strs", type=str, nargs="+", help="Test datasets, none to reuse the validation dataset", ) parser.add_argument( "--epochs", type=int, help="Number of training epochs", ) parser.add_argument( "--batch-size", type=int, help="Batch Size (per GPU)", ) parser.add_argument( "--num-workers", type=int, help="Number de Workers", ) parser.add_argument( "--epoch-length", type=int, help="Length of an epoch in number of iterations", ) parser.add_argument( "--save-checkpoint-frequency", type=int, help="Number of epochs between two named checkpoint saves.", ) parser.add_argument( "--eval-period-iterations", type=int, help="Number of iterations between two evaluations.", ) parser.add_argument( "--learning-rates", nargs="+", type=float, help="Learning rates to grid search.", ) parser.add_argument( "--no-resume", action="store_true", help="Whether to not resume from existing checkpoints", ) parser.add_argument( "--val-metric-type", type=MetricType, choices=list(MetricType), help="Validation metric", ) parser.add_argument( "--test-metric-types", type=MetricType, choices=list(MetricType), nargs="+", help="Evaluation metric", ) parser.add_argument( "--classifier-fpath", type=str, help="Path to a file containing pretrained linear classifiers", ) parser.add_argument( "--val-class-mapping-fpath", type=str, help="Path to a file containing a mapping to adjust classifier outputs", ) parser.add_argument( "--test-class-mapping-fpaths", nargs="+", type=str, help="Path to a file containing a mapping to adjust classifier outputs", ) parser.set_defaults( train_dataset_str="ImageNet:split=TRAIN", val_dataset_str="ImageNet:split=VAL", test_dataset_strs=None, epochs=10, batch_size=128, num_workers=8, epoch_length=1250, save_checkpoint_frequency=20, eval_period_iterations=1250, learning_rates=[1e-5, 2e-5, 5e-5, 1e-4, 2e-4, 5e-4, 1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2, 0.1], val_metric_type=MetricType.MEAN_ACCURACY, test_metric_types=None, classifier_fpath=None, val_class_mapping_fpath=None, test_class_mapping_fpaths=[None], ) return parser def has_ddp_wrapper(m: nn.Module) -> bool: return isinstance(m, DistributedDataParallel) def remove_ddp_wrapper(m: nn.Module) -> nn.Module: return m.module if has_ddp_wrapper(m) else m def _pad_and_collate(batch): maxlen = max(len(targets) for image, targets in batch) padded_batch = [ (image, np.pad(targets, (0, maxlen - len(targets)), constant_values=-1)) for image, targets in batch ] return torch.utils.data.default_collate(padded_batch) def create_linear_input(x_tokens_list, use_n_blocks, use_avgpool): intermediate_output = x_tokens_list[-use_n_blocks:] output = torch.cat([class_token for _, class_token in intermediate_output], dim=-1) if use_avgpool: output = torch.cat( ( output, torch.mean(intermediate_output[-1][0], dim=1), # patch tokens ), dim=-1, ) output = output.reshape(output.shape[0], -1) return output.float() class LinearClassifier(nn.Module): """Linear layer to train on top of frozen features""" def __init__(self, out_dim, use_n_blocks, use_avgpool, num_classes=1000): super().__init__() self.out_dim = out_dim self.use_n_blocks = use_n_blocks self.use_avgpool = use_avgpool self.num_classes = num_classes self.linear = nn.Linear(out_dim, num_classes) self.linear.weight.data.normal_(mean=0.0, std=0.01) self.linear.bias.data.zero_() def forward(self, x_tokens_list): output = create_linear_input(x_tokens_list, self.use_n_blocks, self.use_avgpool) return self.linear(output) class AllClassifiers(nn.Module): def __init__(self, classifiers_dict): super().__init__() self.classifiers_dict = nn.ModuleDict() self.classifiers_dict.update(classifiers_dict) def forward(self, inputs): return {k: v.forward(inputs) for k, v in self.classifiers_dict.items()} def __len__(self): return len(self.classifiers_dict) class LinearPostprocessor(nn.Module): def __init__(self, linear_classifier, class_mapping=None): super().__init__() self.linear_classifier = linear_classifier self.register_buffer("class_mapping", None if class_mapping is None else torch.LongTensor(class_mapping)) def forward(self, samples, targets): preds = self.linear_classifier(samples) return { "preds": preds[:, self.class_mapping] if self.class_mapping is not None else preds, "target": targets, } def scale_lr(learning_rates, batch_size): return learning_rates * (batch_size * distributed.get_global_size()) / 256.0 def setup_linear_classifiers(sample_output, n_last_blocks_list, learning_rates, batch_size, num_classes=1000): linear_classifiers_dict = nn.ModuleDict() optim_param_groups = [] for n in n_last_blocks_list: for avgpool in [False, True]: for _lr in learning_rates: lr = scale_lr(_lr, batch_size) out_dim = create_linear_input(sample_output, use_n_blocks=n, use_avgpool=avgpool).shape[1] linear_classifier = LinearClassifier( out_dim, use_n_blocks=n, use_avgpool=avgpool, num_classes=num_classes ) linear_classifier = linear_classifier.cuda() linear_classifiers_dict[ f"classifier_{n}_blocks_avgpool_{avgpool}_lr_{lr:.5f}".replace(".", "_") ] = linear_classifier optim_param_groups.append({"params": linear_classifier.parameters(), "lr": lr}) linear_classifiers = AllClassifiers(linear_classifiers_dict) if distributed.is_enabled(): linear_classifiers = nn.parallel.DistributedDataParallel(linear_classifiers) return linear_classifiers, optim_param_groups @torch.no_grad() def evaluate_linear_classifiers( feature_model, linear_classifiers, data_loader, metric_type, metrics_file_path, training_num_classes, iteration, prefixstring="", class_mapping=None, best_classifier_on_val=None, ): logger.info("running validation !") num_classes = len(class_mapping) if class_mapping is not None else training_num_classes metric = build_metric(metric_type, num_classes=num_classes) postprocessors = {k: LinearPostprocessor(v, class_mapping) for k, v in linear_classifiers.classifiers_dict.items()} metrics = {k: metric.clone() for k in linear_classifiers.classifiers_dict} _, results_dict_temp = evaluate( feature_model, data_loader, postprocessors, metrics, torch.cuda.current_device(), ) logger.info("") results_dict = {} max_accuracy = 0 best_classifier = "" for i, (classifier_string, metric) in enumerate(results_dict_temp.items()): logger.info(f"{prefixstring} -- Classifier: {classifier_string} * {metric}") if ( best_classifier_on_val is None and metric["top-1"].item() > max_accuracy ) or classifier_string == best_classifier_on_val: max_accuracy = metric["top-1"].item() best_classifier = classifier_string results_dict["best_classifier"] = {"name": best_classifier, "accuracy": max_accuracy} logger.info(f"best classifier: {results_dict['best_classifier']}") if distributed.is_main_process(): with open(metrics_file_path, "a") as f: f.write(f"iter: {iteration}\n") for k, v in results_dict.items(): f.write(json.dumps({k: v}) + "\n") f.write("\n") return results_dict def eval_linear( *, feature_model, linear_classifiers, train_data_loader, val_data_loader, metrics_file_path, optimizer, scheduler, output_dir, max_iter, checkpoint_period, # In number of iter, creates a new file every period running_checkpoint_period, # Period to update main checkpoint file eval_period, metric_type, training_num_classes, resume=True, classifier_fpath=None, val_class_mapping=None, ): checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler) start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1 periodic_checkpointer = PeriodicCheckpointer(checkpointer, checkpoint_period, max_iter=max_iter) iteration = start_iter logger.info("Starting training from iteration {}".format(start_iter)) metric_logger = MetricLogger(delimiter=" ") header = "Training" for data, labels in metric_logger.log_every( train_data_loader, 10, header, max_iter, start_iter, ): data = data.cuda(non_blocking=True) labels = labels.cuda(non_blocking=True) features = feature_model(data) outputs = linear_classifiers(features) losses = {f"loss_{k}": nn.CrossEntropyLoss()(v, labels) for k, v in outputs.items()} loss = sum(losses.values()) # compute the gradients optimizer.zero_grad() loss.backward() # step optimizer.step() scheduler.step() # log if iteration % 10 == 0: torch.cuda.synchronize() metric_logger.update(loss=loss.item()) metric_logger.update(lr=optimizer.param_groups[0]["lr"]) print("lr", optimizer.param_groups[0]["lr"]) if iteration - start_iter > 5: if iteration % running_checkpoint_period == 0: torch.cuda.synchronize() if distributed.is_main_process(): logger.info("Checkpointing running_checkpoint") periodic_checkpointer.save("running_checkpoint_linear_eval", iteration=iteration) torch.cuda.synchronize() periodic_checkpointer.step(iteration) if eval_period > 0 and (iteration + 1) % eval_period == 0 and iteration != max_iter - 1: _ = evaluate_linear_classifiers( feature_model=feature_model, linear_classifiers=remove_ddp_wrapper(linear_classifiers), data_loader=val_data_loader, metrics_file_path=metrics_file_path, prefixstring=f"ITER: {iteration}", metric_type=metric_type, training_num_classes=training_num_classes, iteration=iteration, class_mapping=val_class_mapping, ) torch.cuda.synchronize() iteration = iteration + 1 val_results_dict = evaluate_linear_classifiers( feature_model=feature_model, linear_classifiers=remove_ddp_wrapper(linear_classifiers), data_loader=val_data_loader, metrics_file_path=metrics_file_path, metric_type=metric_type, training_num_classes=training_num_classes, iteration=iteration, class_mapping=val_class_mapping, ) return val_results_dict, feature_model, linear_classifiers, iteration def make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type): test_dataset = make_dataset( dataset_str=test_dataset_str, transform=make_classification_eval_transform(), ) test_data_loader = make_data_loader( dataset=test_dataset, batch_size=batch_size, num_workers=num_workers, sampler_type=SamplerType.DISTRIBUTED, drop_last=False, shuffle=False, persistent_workers=False, collate_fn=_pad_and_collate if metric_type == MetricType.IMAGENET_REAL_ACCURACY else None, ) return test_data_loader def test_on_datasets( feature_model, linear_classifiers, test_dataset_strs, batch_size, num_workers, test_metric_types, metrics_file_path, training_num_classes, iteration, best_classifier_on_val, prefixstring="", test_class_mappings=[None], ): results_dict = {} for test_dataset_str, class_mapping, metric_type in zip(test_dataset_strs, test_class_mappings, test_metric_types): logger.info(f"Testing on {test_dataset_str}") test_data_loader = make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type) dataset_results_dict = evaluate_linear_classifiers( feature_model, remove_ddp_wrapper(linear_classifiers), test_data_loader, metric_type, metrics_file_path, training_num_classes, iteration, prefixstring="", class_mapping=class_mapping, best_classifier_on_val=best_classifier_on_val, ) results_dict[f"{test_dataset_str}_accuracy"] = 100.0 * dataset_results_dict["best_classifier"]["accuracy"] return results_dict def run_eval_linear( model, output_dir, train_dataset_str, val_dataset_str, batch_size, epochs, epoch_length, num_workers, save_checkpoint_frequency, eval_period_iterations, learning_rates, autocast_dtype, test_dataset_strs=None, resume=True, classifier_fpath=None, val_class_mapping_fpath=None, test_class_mapping_fpaths=[None], val_metric_type=MetricType.MEAN_ACCURACY, test_metric_types=None, ): seed = 0 if test_dataset_strs is None: test_dataset_strs = [val_dataset_str] if test_metric_types is None: test_metric_types = [val_metric_type] * len(test_dataset_strs) else: assert len(test_metric_types) == len(test_dataset_strs) assert len(test_dataset_strs) == len(test_class_mapping_fpaths) train_transform = make_classification_train_transform() train_dataset = make_dataset( dataset_str=train_dataset_str, transform=train_transform, ) training_num_classes = len(torch.unique(torch.Tensor(train_dataset.get_targets().astype(int)))) sampler_type = SamplerType.SHARDED_INFINITE # sampler_type = SamplerType.INFINITE n_last_blocks_list = [1, 4] n_last_blocks = max(n_last_blocks_list) autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype) feature_model = ModelWithIntermediateLayers(model, n_last_blocks, autocast_ctx) sample_output = feature_model(train_dataset[0][0].unsqueeze(0).cuda()) linear_classifiers, optim_param_groups = setup_linear_classifiers( sample_output, n_last_blocks_list, learning_rates, batch_size, training_num_classes, ) optimizer = torch.optim.SGD(optim_param_groups, momentum=0.9, weight_decay=0) max_iter = epochs * epoch_length scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_iter, eta_min=0) checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler) start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1 train_data_loader = make_data_loader( dataset=train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True, seed=seed, sampler_type=sampler_type, sampler_advance=start_iter, drop_last=True, persistent_workers=True, ) val_data_loader = make_eval_data_loader(val_dataset_str, batch_size, num_workers, val_metric_type) checkpoint_period = save_checkpoint_frequency * epoch_length if val_class_mapping_fpath is not None: logger.info(f"Using class mapping from {val_class_mapping_fpath}") val_class_mapping = np.load(val_class_mapping_fpath) else: val_class_mapping = None test_class_mappings = [] for class_mapping_fpath in test_class_mapping_fpaths: if class_mapping_fpath is not None and class_mapping_fpath != "None": logger.info(f"Using class mapping from {class_mapping_fpath}") class_mapping = np.load(class_mapping_fpath) else: class_mapping = None test_class_mappings.append(class_mapping) metrics_file_path = os.path.join(output_dir, "results_eval_linear.json") val_results_dict, feature_model, linear_classifiers, iteration = eval_linear( feature_model=feature_model, linear_classifiers=linear_classifiers, train_data_loader=train_data_loader, val_data_loader=val_data_loader, metrics_file_path=metrics_file_path, optimizer=optimizer, scheduler=scheduler, output_dir=output_dir, max_iter=max_iter, checkpoint_period=checkpoint_period, running_checkpoint_period=epoch_length, eval_period=eval_period_iterations, metric_type=val_metric_type, training_num_classes=training_num_classes, resume=resume, val_class_mapping=val_class_mapping, classifier_fpath=classifier_fpath, ) results_dict = {} if len(test_dataset_strs) > 1 or test_dataset_strs[0] != val_dataset_str: results_dict = test_on_datasets( feature_model, linear_classifiers, test_dataset_strs, batch_size, 0, # num_workers, test_metric_types, metrics_file_path, training_num_classes, iteration, val_results_dict["best_classifier"]["name"], prefixstring="", test_class_mappings=test_class_mappings, ) results_dict["best_classifier"] = val_results_dict["best_classifier"]["name"] results_dict[f"{val_dataset_str}_accuracy"] = 100.0 * val_results_dict["best_classifier"]["accuracy"] logger.info("Test Results Dict " + str(results_dict)) return results_dict def main(args): model, autocast_dtype = setup_and_build_model(args) run_eval_linear( model=model, output_dir=args.output_dir, train_dataset_str=args.train_dataset_str, val_dataset_str=args.val_dataset_str, test_dataset_strs=args.test_dataset_strs, batch_size=args.batch_size, epochs=args.epochs, epoch_length=args.epoch_length, num_workers=args.num_workers, save_checkpoint_frequency=args.save_checkpoint_frequency, eval_period_iterations=args.eval_period_iterations, learning_rates=args.learning_rates, autocast_dtype=autocast_dtype, resume=not args.no_resume, classifier_fpath=args.classifier_fpath, val_metric_type=args.val_metric_type, test_metric_types=args.test_metric_types, val_class_mapping_fpath=args.val_class_mapping_fpath, test_class_mapping_fpaths=args.test_class_mapping_fpaths, ) return 0 if __name__ == "__main__": description = "DINOv2 linear evaluation" args_parser = get_args_parser(description=description) args = args_parser.parse_args() sys.exit(main(args)) ================================================ FILE: dinov2/eval/log_regression.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import argparse import gc import logging import sys import time from typing import List, Optional from cuml.linear_model import LogisticRegression import torch import torch.backends.cudnn as cudnn import torch.distributed from torch import nn from torch.utils.data import TensorDataset from torchmetrics import MetricTracker from dinov2.data import make_dataset from dinov2.data.transforms import make_classification_eval_transform from dinov2.distributed import get_global_rank, get_global_size from dinov2.eval.metrics import MetricType, build_metric from dinov2.eval.setup import get_args_parser as get_setup_args_parser from dinov2.eval.setup import setup_and_build_model from dinov2.eval.utils import evaluate, extract_features from dinov2.utils.dtype import as_torch_dtype logger = logging.getLogger("dinov2") DEFAULT_MAX_ITER = 1_000 C_POWER_RANGE = torch.linspace(-6, 5, 45) _CPU_DEVICE = torch.device("cpu") def get_args_parser( description: Optional[str] = None, parents: Optional[List[argparse.ArgumentParser]] = None, add_help: bool = True, ): parents = parents or [] setup_args_parser = get_setup_args_parser(parents=parents, add_help=False) parents = [setup_args_parser] parser = argparse.ArgumentParser( description=description, parents=parents, add_help=add_help, ) parser.add_argument( "--train-dataset", dest="train_dataset_str", type=str, help="Training dataset", ) parser.add_argument( "--val-dataset", dest="val_dataset_str", type=str, help="Validation dataset", ) parser.add_argument( "--finetune-dataset-str", dest="finetune_dataset_str", type=str, help="Fine-tuning dataset", ) parser.add_argument( "--finetune-on-val", action="store_true", help="If there is no finetune dataset, whether to choose the " "hyperparameters on the val set instead of 10%% of the train dataset", ) parser.add_argument( "--metric-type", type=MetricType, choices=list(MetricType), help="Metric type", ) parser.add_argument( "--train-features-device", type=str, help="Device to gather train features (cpu, cuda, cuda:0, etc.), default: %(default)s", ) parser.add_argument( "--train-dtype", type=str, help="Data type to convert the train features to (default: %(default)s)", ) parser.add_argument( "--max-train-iters", type=int, help="Maximum number of train iterations (default: %(default)s)", ) parser.set_defaults( train_dataset_str="ImageNet:split=TRAIN", val_dataset_str="ImageNet:split=VAL", finetune_dataset_str=None, metric_type=MetricType.MEAN_ACCURACY, train_features_device="cpu", train_dtype="float64", max_train_iters=DEFAULT_MAX_ITER, finetune_on_val=False, ) return parser class LogRegModule(nn.Module): def __init__( self, C, max_iter=DEFAULT_MAX_ITER, dtype=torch.float64, device=_CPU_DEVICE, ): super().__init__() self.dtype = dtype self.device = device self.estimator = LogisticRegression( penalty="l2", C=C, max_iter=max_iter, output_type="numpy", tol=1e-12, linesearch_max_iter=50, ) def forward(self, samples, targets): samples_device = samples.device samples = samples.to(dtype=self.dtype, device=self.device) if self.device == _CPU_DEVICE: samples = samples.numpy() probas = self.estimator.predict_proba(samples) return {"preds": torch.from_numpy(probas).to(samples_device), "target": targets} def fit(self, train_features, train_labels): train_features = train_features.to(dtype=self.dtype, device=self.device) train_labels = train_labels.to(dtype=self.dtype, device=self.device) if self.device == _CPU_DEVICE: # both cuML and sklearn only work with numpy arrays on CPU train_features = train_features.numpy() train_labels = train_labels.numpy() self.estimator.fit(train_features, train_labels) def evaluate_model(*, logreg_model, logreg_metric, test_data_loader, device): postprocessors = {"metrics": logreg_model} metrics = {"metrics": logreg_metric} return evaluate(nn.Identity(), test_data_loader, postprocessors, metrics, device) def train_for_C(*, C, max_iter, train_features, train_labels, dtype=torch.float64, device=_CPU_DEVICE): logreg_model = LogRegModule(C, max_iter=max_iter, dtype=dtype, device=device) logreg_model.fit(train_features, train_labels) return logreg_model def train_and_evaluate( *, C, max_iter, train_features, train_labels, logreg_metric, test_data_loader, train_dtype=torch.float64, train_features_device, eval_device, ): logreg_model = train_for_C( C=C, max_iter=max_iter, train_features=train_features, train_labels=train_labels, dtype=train_dtype, device=train_features_device, ) return evaluate_model( logreg_model=logreg_model, logreg_metric=logreg_metric, test_data_loader=test_data_loader, device=eval_device, ) def sweep_C_values( *, train_features, train_labels, test_data_loader, metric_type, num_classes, train_dtype=torch.float64, train_features_device=_CPU_DEVICE, max_train_iters=DEFAULT_MAX_ITER, ): if metric_type == MetricType.PER_CLASS_ACCURACY: # If we want to output per-class accuracy, we select the hyperparameters with mean per class metric_type = MetricType.MEAN_PER_CLASS_ACCURACY logreg_metric = build_metric(metric_type, num_classes=num_classes) metric_tracker = MetricTracker(logreg_metric, maximize=True) ALL_C = 10**C_POWER_RANGE logreg_models = {} train_features = train_features.to(dtype=train_dtype, device=train_features_device) train_labels = train_labels.to(device=train_features_device) for i in range(get_global_rank(), len(ALL_C), get_global_size()): C = ALL_C[i].item() logger.info( f"Training for C = {C:.5f}, dtype={train_dtype}, " f"features: {train_features.shape}, {train_features.dtype}, " f"labels: {train_labels.shape}, {train_labels.dtype}" ) logreg_models[C] = train_for_C( C=C, max_iter=max_train_iters, train_features=train_features, train_labels=train_labels, dtype=train_dtype, device=train_features_device, ) gather_list = [None for _ in range(get_global_size())] torch.distributed.all_gather_object(gather_list, logreg_models) logreg_models_gathered = {} for logreg_dict in gather_list: logreg_models_gathered.update(logreg_dict) for i in range(len(ALL_C)): metric_tracker.increment() C = ALL_C[i].item() evals = evaluate_model( logreg_model=logreg_models_gathered[C], logreg_metric=metric_tracker, test_data_loader=test_data_loader, device=torch.cuda.current_device(), ) logger.info(f"Trained for C = {C:.5f}, accuracies = {evals}") best_stats, which_epoch = metric_tracker.best_metric(return_step=True) best_stats_100 = {k: 100.0 * v for k, v in best_stats.items()} if which_epoch["top-1"] == i: best_C = C logger.info(f"Sweep best {best_stats_100}, best C = {best_C:.6f}") return best_stats, best_C def eval_log_regression( *, model, train_dataset, val_dataset, finetune_dataset, metric_type, batch_size, num_workers, finetune_on_val=False, train_dtype=torch.float64, train_features_device=_CPU_DEVICE, max_train_iters=DEFAULT_MAX_ITER, ): """ Implements the "standard" process for log regression evaluation: The value of C is chosen by training on train_dataset and evaluating on finetune_dataset. Then, the final model is trained on a concatenation of train_dataset and finetune_dataset, and is evaluated on val_dataset. If there is no finetune_dataset, the value of C is the one that yields the best results on a random 10% subset of the train dataset """ start = time.time() train_features, train_labels = extract_features( model, train_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE) ) val_features, val_labels = extract_features( model, val_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE) ) val_data_loader = torch.utils.data.DataLoader( TensorDataset(val_features, val_labels), batch_size=batch_size, drop_last=False, num_workers=0, persistent_workers=False, ) if finetune_dataset is None and finetune_on_val: logger.info("Choosing hyperparameters on the val dataset") finetune_features, finetune_labels = val_features, val_labels elif finetune_dataset is None and not finetune_on_val: logger.info("Choosing hyperparameters on 10% of the train dataset") torch.manual_seed(0) indices = torch.randperm(len(train_features), device=train_features.device) finetune_index = indices[: len(train_features) // 10] train_index = indices[len(train_features) // 10 :] finetune_features, finetune_labels = train_features[finetune_index], train_labels[finetune_index] train_features, train_labels = train_features[train_index], train_labels[train_index] else: logger.info("Choosing hyperparameters on the finetune dataset") finetune_features, finetune_labels = extract_features( model, finetune_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE) ) # release the model - free GPU memory del model gc.collect() torch.cuda.empty_cache() finetune_data_loader = torch.utils.data.DataLoader( TensorDataset(finetune_features, finetune_labels), batch_size=batch_size, drop_last=False, ) if len(train_labels.shape) > 1: num_classes = train_labels.shape[1] else: num_classes = train_labels.max() + 1 logger.info("Using cuML for logistic regression") best_stats, best_C = sweep_C_values( train_features=train_features, train_labels=train_labels, test_data_loader=finetune_data_loader, metric_type=metric_type, num_classes=num_classes, train_dtype=train_dtype, train_features_device=train_features_device, max_train_iters=max_train_iters, ) if not finetune_on_val: logger.info("Best parameter found, concatenating features") train_features = torch.cat((train_features, finetune_features)) train_labels = torch.cat((train_labels, finetune_labels)) logger.info("Training final model") logreg_metric = build_metric(metric_type, num_classes=num_classes) evals = train_and_evaluate( C=best_C, max_iter=max_train_iters, train_features=train_features, train_labels=train_labels, logreg_metric=logreg_metric.clone(), test_data_loader=val_data_loader, eval_device=torch.cuda.current_device(), train_dtype=train_dtype, train_features_device=train_features_device, ) best_stats = evals[1]["metrics"] best_stats["best_C"] = best_C logger.info(f"Log regression evaluation done in {int(time.time() - start)}s") return best_stats def eval_log_regression_with_model( model, train_dataset_str="ImageNet:split=TRAIN", val_dataset_str="ImageNet:split=VAL", finetune_dataset_str=None, autocast_dtype=torch.float, finetune_on_val=False, metric_type=MetricType.MEAN_ACCURACY, train_dtype=torch.float64, train_features_device=_CPU_DEVICE, max_train_iters=DEFAULT_MAX_ITER, ): cudnn.benchmark = True transform = make_classification_eval_transform(resize_size=224) target_transform = None train_dataset = make_dataset(dataset_str=train_dataset_str, transform=transform, target_transform=target_transform) val_dataset = make_dataset(dataset_str=val_dataset_str, transform=transform, target_transform=target_transform) if finetune_dataset_str is not None: finetune_dataset = make_dataset( dataset_str=finetune_dataset_str, transform=transform, target_transform=target_transform ) else: finetune_dataset = None with torch.cuda.amp.autocast(dtype=autocast_dtype): results_dict_logreg = eval_log_regression( model=model, train_dataset=train_dataset, val_dataset=val_dataset, finetune_dataset=finetune_dataset, metric_type=metric_type, batch_size=256, num_workers=0, # 5, finetune_on_val=finetune_on_val, train_dtype=train_dtype, train_features_device=train_features_device, max_train_iters=max_train_iters, ) results_dict = { "top-1": results_dict_logreg["top-1"].cpu().numpy() * 100.0, "top-5": results_dict_logreg.get("top-5", torch.tensor(0.0)).cpu().numpy() * 100.0, "best_C": results_dict_logreg["best_C"], } logger.info( "\n".join( [ "Training of the supervised logistic regression on frozen features completed.\n" "Top-1 test accuracy: {acc:.1f}".format(acc=results_dict["top-1"]), "Top-5 test accuracy: {acc:.1f}".format(acc=results_dict["top-5"]), "obtained for C = {c:.6f}".format(c=results_dict["best_C"]), ] ) ) torch.distributed.barrier() return results_dict def main(args): model, autocast_dtype = setup_and_build_model(args) eval_log_regression_with_model( model=model, train_dataset_str=args.train_dataset_str, val_dataset_str=args.val_dataset_str, finetune_dataset_str=args.finetune_dataset_str, autocast_dtype=autocast_dtype, finetune_on_val=args.finetune_on_val, metric_type=args.metric_type, train_dtype=as_torch_dtype(args.train_dtype), train_features_device=torch.device(args.train_features_device), max_train_iters=args.max_train_iters, ) return 0 if __name__ == "__main__": description = "DINOv2 logistic regression evaluation" args_parser = get_args_parser(description=description) args = args_parser.parse_args() sys.exit(main(args)) ================================================ FILE: dinov2/eval/metrics.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from enum import Enum import logging from typing import Any, Dict, Optional import torch from torch import Tensor from torchmetrics import Metric, MetricCollection from torchmetrics.classification import MulticlassAccuracy, MulticlassF1Score, MultilabelF1Score from torchmetrics.utilities.data import dim_zero_cat, select_topk logger = logging.getLogger("dinov2") class MetricType(Enum): MEAN_ACCURACY = "mean_accuracy" MEAN_PER_CLASS_ACCURACY = "mean_per_class_accuracy" PER_CLASS_ACCURACY = "per_class_accuracy" IMAGENET_REAL_ACCURACY = "imagenet_real_accuracy" MEAN_PER_CLASS_MULTICLASS_F1 = "mean_per_class_multiclass_f1" MEAN_PER_CLASS_MULTILABEL_F1 = "mean_per_class_multilabel_f1" @property def accuracy_averaging(self): return getattr(AccuracyAveraging, self.name, None) def __str__(self): return self.value class AccuracyAveraging(Enum): MEAN_ACCURACY = "micro" MEAN_PER_CLASS_ACCURACY = "macro" PER_CLASS_ACCURACY = "none" def __str__(self): return self.value def build_metric(metric_type: MetricType, *, num_classes: int, ks: Optional[tuple] = None): if metric_type.accuracy_averaging is not None: return build_topk_accuracy_metric( average_type=metric_type.accuracy_averaging, num_classes=num_classes, ks=(1, 5) if ks is None else ks, ) elif metric_type == MetricType.IMAGENET_REAL_ACCURACY: return build_topk_imagenet_real_accuracy_metric( num_classes=num_classes, ks=(1, 5) if ks is None else ks, ) elif metric_type == MetricType.MEAN_PER_CLASS_MULTILABEL_F1: return MetricCollection({"top-1": MultilabelF1Score(num_labels=int(num_classes), average="macro")}) elif metric_type == MetricType.MEAN_PER_CLASS_MULTICLASS_F1: return MetricCollection({"top-1": MulticlassF1Score(num_classes=int(num_classes), average="macro")}) raise ValueError(f"Unknown metric type {metric_type}") def build_topk_accuracy_metric(average_type: AccuracyAveraging, num_classes: int, ks: tuple = (1, 5)): metrics: Dict[str, Metric] = { f"top-{k}": MulticlassAccuracy(top_k=k, num_classes=int(num_classes), average=average_type.value) for k in ks } return MetricCollection(metrics) def build_topk_imagenet_real_accuracy_metric(num_classes: int, ks: tuple = (1, 5)): metrics: Dict[str, Metric] = {f"top-{k}": ImageNetReaLAccuracy(top_k=k, num_classes=int(num_classes)) for k in ks} return MetricCollection(metrics) class ImageNetReaLAccuracy(Metric): is_differentiable: bool = False higher_is_better: Optional[bool] = None full_state_update: bool = False def __init__( self, num_classes: int, top_k: int = 1, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.num_classes = num_classes self.top_k = top_k self.add_state("tp", [], dist_reduce_fx="cat") def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore # preds [B, D] # target [B, A] # preds_oh [B, D] with 0 and 1 # select top K highest probabilities, use one hot representation preds_oh = select_topk(preds, self.top_k) # target_oh [B, D + 1] with 0 and 1 target_oh = torch.zeros((preds_oh.shape[0], preds_oh.shape[1] + 1), device=target.device, dtype=torch.int32) target = target.long() # for undefined targets (-1) use a fake value `num_classes` target[target == -1] = self.num_classes # fill targets, use one hot representation target_oh.scatter_(1, target, 1) # target_oh [B, D] (remove the fake target at index `num_classes`) target_oh = target_oh[:, :-1] # tp [B] with 0 and 1 tp = (preds_oh * target_oh == 1).sum(dim=1) # at least one match between prediction and target tp.clip_(max=1) # ignore instances where no targets are defined mask = target_oh.sum(dim=1) > 0 tp = tp[mask] self.tp.append(tp) # type: ignore def compute(self) -> Tensor: tp = dim_zero_cat(self.tp) # type: ignore return tp.float().mean() ================================================ FILE: dinov2/eval/segmentation/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. ================================================ FILE: dinov2/eval/segmentation/hooks/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .optimizer import DistOptimizerHook ================================================ FILE: dinov2/eval/segmentation/hooks/optimizer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. try: import apex except ImportError: print("apex is not installed") from mmcv.runner import OptimizerHook, HOOKS @HOOKS.register_module() class DistOptimizerHook(OptimizerHook): """Optimizer hook for distributed training.""" def __init__(self, update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=-1, use_fp16=False): self.grad_clip = grad_clip self.coalesce = coalesce self.bucket_size_mb = bucket_size_mb self.update_interval = update_interval self.use_fp16 = use_fp16 def before_run(self, runner): runner.optimizer.zero_grad() def after_train_iter(self, runner): runner.outputs["loss"] /= self.update_interval if self.use_fp16: # runner.outputs['loss'].backward() with apex.amp.scale_loss(runner.outputs["loss"], runner.optimizer) as scaled_loss: scaled_loss.backward() else: runner.outputs["loss"].backward() if self.every_n_iters(runner, self.update_interval): if self.grad_clip is not None: self.clip_grads(runner.model.parameters()) runner.optimizer.step() runner.optimizer.zero_grad() ================================================ FILE: dinov2/eval/segmentation/models/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .backbones import * # noqa: F403 from .decode_heads import * # noqa: F403 ================================================ FILE: dinov2/eval/segmentation/models/backbones/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .vision_transformer import DinoVisionTransformer ================================================ FILE: dinov2/eval/segmentation/models/backbones/vision_transformer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from mmcv.runner import BaseModule from mmseg.models.builder import BACKBONES @BACKBONES.register_module() class DinoVisionTransformer(BaseModule): """Vision Transformer.""" def __init__( self, *args, **kwargs, ): super().__init__() ================================================ FILE: dinov2/eval/segmentation/models/decode_heads/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .linear_head import BNHead ================================================ FILE: dinov2/eval/segmentation/models/decode_heads/linear_head.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn from mmseg.models.builder import HEADS from mmseg.models.decode_heads.decode_head import BaseDecodeHead from mmseg.ops import resize @HEADS.register_module() class BNHead(BaseDecodeHead): """Just a batchnorm.""" def __init__(self, resize_factors=None, **kwargs): super().__init__(**kwargs) assert self.in_channels == self.channels self.bn = nn.SyncBatchNorm(self.in_channels) self.resize_factors = resize_factors def _forward_feature(self, inputs): """Forward function for feature maps before classifying each pixel with ``self.cls_seg`` fc. Args: inputs (list[Tensor]): List of multi-level img features. Returns: feats (Tensor): A tensor of shape (batch_size, self.channels, H, W) which is feature map for last layer of decoder head. """ # print("inputs", [i.shape for i in inputs]) x = self._transform_inputs(inputs) # print("x", x.shape) feats = self.bn(x) # print("feats", feats.shape) return feats def _transform_inputs(self, inputs): """Transform inputs for decoder. Args: inputs (list[Tensor]): List of multi-level img features. Returns: Tensor: The transformed inputs """ if self.input_transform == "resize_concat": # accept lists (for cls token) input_list = [] for x in inputs: if isinstance(x, list): input_list.extend(x) else: input_list.append(x) inputs = input_list # an image descriptor can be a local descriptor with resolution 1x1 for i, x in enumerate(inputs): if len(x.shape) == 2: inputs[i] = x[:, :, None, None] # select indices inputs = [inputs[i] for i in self.in_index] # Resizing shenanigans # print("before", *(x.shape for x in inputs)) if self.resize_factors is not None: assert len(self.resize_factors) == len(inputs), (len(self.resize_factors), len(inputs)) inputs = [ resize(input=x, scale_factor=f, mode="bilinear" if f >= 1 else "area") for x, f in zip(inputs, self.resize_factors) ] # print("after", *(x.shape for x in inputs)) upsampled_inputs = [ resize(input=x, size=inputs[0].shape[2:], mode="bilinear", align_corners=self.align_corners) for x in inputs ] inputs = torch.cat(upsampled_inputs, dim=1) elif self.input_transform == "multiple_select": inputs = [inputs[i] for i in self.in_index] else: inputs = inputs[self.in_index] return inputs def forward(self, inputs): """Forward function.""" output = self._forward_feature(inputs) output = self.cls_seg(output) return output ================================================ FILE: dinov2/eval/segmentation/utils/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. ================================================ FILE: dinov2/eval/segmentation/utils/colormaps.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. ADE20K_COLORMAP = [ (0, 0, 0), (120, 120, 120), (180, 120, 120), (6, 230, 230), (80, 50, 50), (4, 200, 3), (120, 120, 80), (140, 140, 140), (204, 5, 255), (230, 230, 230), (4, 250, 7), (224, 5, 255), (235, 255, 7), (150, 5, 61), (120, 120, 70), (8, 255, 51), (255, 6, 82), (143, 255, 140), (204, 255, 4), (255, 51, 7), (204, 70, 3), (0, 102, 200), (61, 230, 250), (255, 6, 51), (11, 102, 255), (255, 7, 71), (255, 9, 224), (9, 7, 230), (220, 220, 220), (255, 9, 92), (112, 9, 255), (8, 255, 214), (7, 255, 224), (255, 184, 6), (10, 255, 71), (255, 41, 10), (7, 255, 255), (224, 255, 8), (102, 8, 255), (255, 61, 6), (255, 194, 7), (255, 122, 8), (0, 255, 20), (255, 8, 41), (255, 5, 153), (6, 51, 255), (235, 12, 255), (160, 150, 20), (0, 163, 255), (140, 140, 140), (250, 10, 15), (20, 255, 0), (31, 255, 0), (255, 31, 0), (255, 224, 0), (153, 255, 0), (0, 0, 255), (255, 71, 0), (0, 235, 255), (0, 173, 255), (31, 0, 255), (11, 200, 200), (255, 82, 0), (0, 255, 245), (0, 61, 255), (0, 255, 112), (0, 255, 133), (255, 0, 0), (255, 163, 0), (255, 102, 0), (194, 255, 0), (0, 143, 255), (51, 255, 0), (0, 82, 255), (0, 255, 41), (0, 255, 173), (10, 0, 255), (173, 255, 0), (0, 255, 153), (255, 92, 0), (255, 0, 255), (255, 0, 245), (255, 0, 102), (255, 173, 0), (255, 0, 20), (255, 184, 184), (0, 31, 255), (0, 255, 61), (0, 71, 255), (255, 0, 204), (0, 255, 194), (0, 255, 82), (0, 10, 255), (0, 112, 255), (51, 0, 255), (0, 194, 255), (0, 122, 255), (0, 255, 163), (255, 153, 0), (0, 255, 10), (255, 112, 0), (143, 255, 0), (82, 0, 255), (163, 255, 0), (255, 235, 0), (8, 184, 170), (133, 0, 255), (0, 255, 92), (184, 0, 255), (255, 0, 31), (0, 184, 255), (0, 214, 255), (255, 0, 112), (92, 255, 0), (0, 224, 255), (112, 224, 255), (70, 184, 160), (163, 0, 255), (153, 0, 255), (71, 255, 0), (255, 0, 163), (255, 204, 0), (255, 0, 143), (0, 255, 235), (133, 255, 0), (255, 0, 235), (245, 0, 255), (255, 0, 122), (255, 245, 0), (10, 190, 212), (214, 255, 0), (0, 204, 255), (20, 0, 255), (255, 255, 0), (0, 153, 255), (0, 41, 255), (0, 255, 204), (41, 0, 255), (41, 255, 0), (173, 0, 255), (0, 245, 255), (71, 0, 255), (122, 0, 255), (0, 255, 184), (0, 92, 255), (184, 255, 0), (0, 133, 255), (255, 214, 0), (25, 194, 194), (102, 255, 0), (92, 0, 255), ] ADE20K_CLASS_NAMES = [ "", "wall", "building;edifice", "sky", "floor;flooring", "tree", "ceiling", "road;route", "bed", "windowpane;window", "grass", "cabinet", "sidewalk;pavement", "person;individual;someone;somebody;mortal;soul", "earth;ground", "door;double;door", "table", "mountain;mount", "plant;flora;plant;life", "curtain;drape;drapery;mantle;pall", "chair", "car;auto;automobile;machine;motorcar", "water", "painting;picture", "sofa;couch;lounge", "shelf", "house", "sea", "mirror", "rug;carpet;carpeting", "field", "armchair", "seat", "fence;fencing", "desk", "rock;stone", "wardrobe;closet;press", "lamp", "bathtub;bathing;tub;bath;tub", "railing;rail", "cushion", "base;pedestal;stand", "box", "column;pillar", "signboard;sign", "chest;of;drawers;chest;bureau;dresser", "counter", "sand", "sink", "skyscraper", "fireplace;hearth;open;fireplace", "refrigerator;icebox", "grandstand;covered;stand", "path", "stairs;steps", "runway", "case;display;case;showcase;vitrine", "pool;table;billiard;table;snooker;table", "pillow", "screen;door;screen", "stairway;staircase", "river", "bridge;span", "bookcase", "blind;screen", "coffee;table;cocktail;table", "toilet;can;commode;crapper;pot;potty;stool;throne", "flower", "book", "hill", "bench", "countertop", "stove;kitchen;stove;range;kitchen;range;cooking;stove", "palm;palm;tree", "kitchen;island", "computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system", "swivel;chair", "boat", "bar", "arcade;machine", "hovel;hut;hutch;shack;shanty", "bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle", "towel", "light;light;source", "truck;motortruck", "tower", "chandelier;pendant;pendent", "awning;sunshade;sunblind", "streetlight;street;lamp", "booth;cubicle;stall;kiosk", "television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box", "airplane;aeroplane;plane", "dirt;track", "apparel;wearing;apparel;dress;clothes", "pole", "land;ground;soil", "bannister;banister;balustrade;balusters;handrail", "escalator;moving;staircase;moving;stairway", "ottoman;pouf;pouffe;puff;hassock", "bottle", "buffet;counter;sideboard", "poster;posting;placard;notice;bill;card", "stage", "van", "ship", "fountain", "conveyer;belt;conveyor;belt;conveyer;conveyor;transporter", "canopy", "washer;automatic;washer;washing;machine", "plaything;toy", "swimming;pool;swimming;bath;natatorium", "stool", "barrel;cask", "basket;handbasket", "waterfall;falls", "tent;collapsible;shelter", "bag", "minibike;motorbike", "cradle", "oven", "ball", "food;solid;food", "step;stair", "tank;storage;tank", "trade;name;brand;name;brand;marque", "microwave;microwave;oven", "pot;flowerpot", "animal;animate;being;beast;brute;creature;fauna", "bicycle;bike;wheel;cycle", "lake", "dishwasher;dish;washer;dishwashing;machine", "screen;silver;screen;projection;screen", "blanket;cover", "sculpture", "hood;exhaust;hood", "sconce", "vase", "traffic;light;traffic;signal;stoplight", "tray", "ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin", "fan", "pier;wharf;wharfage;dock", "crt;screen", "plate", "monitor;monitoring;device", "bulletin;board;notice;board", "shower", "radiator", "glass;drinking;glass", "clock", "flag", ] VOC2012_COLORMAP = [ (0, 0, 0), (128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128), (0, 128, 128), (128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0), (192, 128, 0), (64, 0, 128), (192, 0, 128), (64, 128, 128), (192, 128, 128), (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128), ] VOC2012_CLASS_NAMES = [ "", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor", ] ================================================ FILE: dinov2/eval/segmentation_m2f/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .core import * # noqa: F403 from .models import * # noqa: F403 from .ops import * # noqa: F403 ================================================ FILE: dinov2/eval/segmentation_m2f/core/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from mmseg.core.evaluation import * # noqa: F403 from mmseg.core.seg import * # noqa: F403 from .anchor import * # noqa: F403 from .box import * # noqa: F403 from .utils import * # noqa: F403 ================================================ FILE: dinov2/eval/segmentation_m2f/core/anchor/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .point_generator import MlvlPointGenerator # noqa: F403 ================================================ FILE: dinov2/eval/segmentation_m2f/core/anchor/builder.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import warnings from mmcv.utils import Registry, build_from_cfg PRIOR_GENERATORS = Registry("Generator for anchors and points") ANCHOR_GENERATORS = PRIOR_GENERATORS def build_prior_generator(cfg, default_args=None): return build_from_cfg(cfg, PRIOR_GENERATORS, default_args) def build_anchor_generator(cfg, default_args=None): warnings.warn("``build_anchor_generator`` would be deprecated soon, please use " "``build_prior_generator`` ") return build_prior_generator(cfg, default_args=default_args) ================================================ FILE: dinov2/eval/segmentation_m2f/core/anchor/point_generator.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import numpy as np import torch from torch.nn.modules.utils import _pair from .builder import PRIOR_GENERATORS @PRIOR_GENERATORS.register_module() class MlvlPointGenerator: """Standard points generator for multi-level (Mlvl) feature maps in 2D points-based detectors. Args: strides (list[int] | list[tuple[int, int]]): Strides of anchors in multiple feature levels in order (w, h). offset (float): The offset of points, the value is normalized with corresponding stride. Defaults to 0.5. """ def __init__(self, strides, offset=0.5): self.strides = [_pair(stride) for stride in strides] self.offset = offset @property def num_levels(self): """int: number of feature levels that the generator will be applied""" return len(self.strides) @property def num_base_priors(self): """list[int]: The number of priors (points) at a point on the feature grid""" return [1 for _ in range(len(self.strides))] def _meshgrid(self, x, y, row_major=True): yy, xx = torch.meshgrid(y, x) if row_major: # warning .flatten() would cause error in ONNX exporting # have to use reshape here return xx.reshape(-1), yy.reshape(-1) else: return yy.reshape(-1), xx.reshape(-1) def grid_priors(self, featmap_sizes, dtype=torch.float32, device="cuda", with_stride=False): """Generate grid points of multiple feature levels. Args: featmap_sizes (list[tuple]): List of feature map sizes in multiple feature levels, each size arrange as as (h, w). dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32. device (str): The device where the anchors will be put on. with_stride (bool): Whether to concatenate the stride to the last dimension of points. Return: list[torch.Tensor]: Points of multiple feature levels. The sizes of each tensor should be (N, 2) when with stride is ``False``, where N = width * height, width and height are the sizes of the corresponding feature level, and the last dimension 2 represent (coord_x, coord_y), otherwise the shape should be (N, 4), and the last dimension 4 represent (coord_x, coord_y, stride_w, stride_h). """ assert self.num_levels == len(featmap_sizes) multi_level_priors = [] for i in range(self.num_levels): priors = self.single_level_grid_priors( featmap_sizes[i], level_idx=i, dtype=dtype, device=device, with_stride=with_stride ) multi_level_priors.append(priors) return multi_level_priors def single_level_grid_priors(self, featmap_size, level_idx, dtype=torch.float32, device="cuda", with_stride=False): """Generate grid Points of a single level. Note: This function is usually called by method ``self.grid_priors``. Args: featmap_size (tuple[int]): Size of the feature maps, arrange as (h, w). level_idx (int): The index of corresponding feature map level. dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32. device (str, optional): The device the tensor will be put on. Defaults to 'cuda'. with_stride (bool): Concatenate the stride to the last dimension of points. Return: Tensor: Points of single feature levels. The shape of tensor should be (N, 2) when with stride is ``False``, where N = width * height, width and height are the sizes of the corresponding feature level, and the last dimension 2 represent (coord_x, coord_y), otherwise the shape should be (N, 4), and the last dimension 4 represent (coord_x, coord_y, stride_w, stride_h). """ feat_h, feat_w = featmap_size stride_w, stride_h = self.strides[level_idx] shift_x = (torch.arange(0, feat_w, device=device) + self.offset) * stride_w # keep featmap_size as Tensor instead of int, so that we # can convert to ONNX correctly shift_x = shift_x.to(dtype) shift_y = (torch.arange(0, feat_h, device=device) + self.offset) * stride_h # keep featmap_size as Tensor instead of int, so that we # can convert to ONNX correctly shift_y = shift_y.to(dtype) shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) if not with_stride: shifts = torch.stack([shift_xx, shift_yy], dim=-1) else: # use `shape[0]` instead of `len(shift_xx)` for ONNX export stride_w = shift_xx.new_full((shift_xx.shape[0],), stride_w).to(dtype) stride_h = shift_xx.new_full((shift_yy.shape[0],), stride_h).to(dtype) shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h], dim=-1) all_points = shifts.to(device) return all_points def valid_flags(self, featmap_sizes, pad_shape, device="cuda"): """Generate valid flags of points of multiple feature levels. Args: featmap_sizes (list(tuple)): List of feature map sizes in multiple feature levels, each size arrange as as (h, w). pad_shape (tuple(int)): The padded shape of the image, arrange as (h, w). device (str): The device where the anchors will be put on. Return: list(torch.Tensor): Valid flags of points of multiple levels. """ assert self.num_levels == len(featmap_sizes) multi_level_flags = [] for i in range(self.num_levels): point_stride = self.strides[i] feat_h, feat_w = featmap_sizes[i] h, w = pad_shape[:2] valid_feat_h = min(int(np.ceil(h / point_stride[1])), feat_h) valid_feat_w = min(int(np.ceil(w / point_stride[0])), feat_w) flags = self.single_level_valid_flags((feat_h, feat_w), (valid_feat_h, valid_feat_w), device=device) multi_level_flags.append(flags) return multi_level_flags def single_level_valid_flags(self, featmap_size, valid_size, device="cuda"): """Generate the valid flags of points of a single feature map. Args: featmap_size (tuple[int]): The size of feature maps, arrange as as (h, w). valid_size (tuple[int]): The valid size of the feature maps. The size arrange as as (h, w). device (str, optional): The device where the flags will be put on. Defaults to 'cuda'. Returns: torch.Tensor: The valid flags of each points in a single level \ feature map. """ feat_h, feat_w = featmap_size valid_h, valid_w = valid_size assert valid_h <= feat_h and valid_w <= feat_w valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device) valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device) valid_x[:valid_w] = 1 valid_y[:valid_h] = 1 valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) valid = valid_xx & valid_yy return valid def sparse_priors(self, prior_idxs, featmap_size, level_idx, dtype=torch.float32, device="cuda"): """Generate sparse points according to the ``prior_idxs``. Args: prior_idxs (Tensor): The index of corresponding anchors in the feature map. featmap_size (tuple[int]): feature map size arrange as (w, h). level_idx (int): The level index of corresponding feature map. dtype (obj:`torch.dtype`): Date type of points. Defaults to ``torch.float32``. device (obj:`torch.device`): The device where the points is located. Returns: Tensor: Anchor with shape (N, 2), N should be equal to the length of ``prior_idxs``. And last dimension 2 represent (coord_x, coord_y). """ height, width = featmap_size x = (prior_idxs % width + self.offset) * self.strides[level_idx][0] y = ((prior_idxs // width) % height + self.offset) * self.strides[level_idx][1] prioris = torch.stack([x, y], 1).to(dtype) prioris = prioris.to(device) return prioris ================================================ FILE: dinov2/eval/segmentation_m2f/core/box/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .builder import * # noqa: F403 from .samplers import MaskPseudoSampler # noqa: F403 ================================================ FILE: dinov2/eval/segmentation_m2f/core/box/builder.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from mmcv.utils import Registry, build_from_cfg BBOX_SAMPLERS = Registry("bbox_sampler") BBOX_CODERS = Registry("bbox_coder") def build_sampler(cfg, **default_args): """Builder of box sampler.""" return build_from_cfg(cfg, BBOX_SAMPLERS, default_args) def build_bbox_coder(cfg, **default_args): """Builder of box coder.""" return build_from_cfg(cfg, BBOX_CODERS, default_args) ================================================ FILE: dinov2/eval/segmentation_m2f/core/box/samplers/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .mask_pseudo_sampler import MaskPseudoSampler # noqa: F403 ================================================ FILE: dinov2/eval/segmentation_m2f/core/box/samplers/base_sampler.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from abc import ABCMeta, abstractmethod import torch from .sampling_result import SamplingResult class BaseSampler(metaclass=ABCMeta): """Base class of samplers.""" def __init__(self, num, pos_fraction, neg_pos_ub=-1, add_gt_as_proposals=True, **kwargs): self.num = num self.pos_fraction = pos_fraction self.neg_pos_ub = neg_pos_ub self.add_gt_as_proposals = add_gt_as_proposals self.pos_sampler = self self.neg_sampler = self @abstractmethod def _sample_pos(self, assign_result, num_expected, **kwargs): """Sample positive samples.""" pass @abstractmethod def _sample_neg(self, assign_result, num_expected, **kwargs): """Sample negative samples.""" pass def sample(self, assign_result, bboxes, gt_bboxes, gt_labels=None, **kwargs): """Sample positive and negative bboxes. This is a simple implementation of bbox sampling given candidates, assigning results and ground truth bboxes. Args: assign_result (:obj:`AssignResult`): Bbox assigning results. bboxes (Tensor): Boxes to be sampled from. gt_bboxes (Tensor): Ground truth bboxes. gt_labels (Tensor, optional): Class labels of ground truth bboxes. Returns: :obj:`SamplingResult`: Sampling result. Example: >>> from mmdet.core.bbox import RandomSampler >>> from mmdet.core.bbox import AssignResult >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes >>> rng = ensure_rng(None) >>> assign_result = AssignResult.random(rng=rng) >>> bboxes = random_boxes(assign_result.num_preds, rng=rng) >>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng) >>> gt_labels = None >>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1, >>> add_gt_as_proposals=False) >>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels) """ if len(bboxes.shape) < 2: bboxes = bboxes[None, :] bboxes = bboxes[:, :4] gt_flags = bboxes.new_zeros((bboxes.shape[0],), dtype=torch.uint8) if self.add_gt_as_proposals and len(gt_bboxes) > 0: if gt_labels is None: raise ValueError("gt_labels must be given when add_gt_as_proposals is True") bboxes = torch.cat([gt_bboxes, bboxes], dim=0) assign_result.add_gt_(gt_labels) gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) gt_flags = torch.cat([gt_ones, gt_flags]) num_expected_pos = int(self.num * self.pos_fraction) pos_inds = self.pos_sampler._sample_pos(assign_result, num_expected_pos, bboxes=bboxes, **kwargs) # We found that sampled indices have duplicated items occasionally. # (may be a bug of PyTorch) pos_inds = pos_inds.unique() num_sampled_pos = pos_inds.numel() num_expected_neg = self.num - num_sampled_pos if self.neg_pos_ub >= 0: _pos = max(1, num_sampled_pos) neg_upper_bound = int(self.neg_pos_ub * _pos) if num_expected_neg > neg_upper_bound: num_expected_neg = neg_upper_bound neg_inds = self.neg_sampler._sample_neg(assign_result, num_expected_neg, bboxes=bboxes, **kwargs) neg_inds = neg_inds.unique() sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags) return sampling_result ================================================ FILE: dinov2/eval/segmentation_m2f/core/box/samplers/mask_pseudo_sampler.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py import torch from ..builder import BBOX_SAMPLERS from .base_sampler import BaseSampler from .mask_sampling_result import MaskSamplingResult @BBOX_SAMPLERS.register_module() class MaskPseudoSampler(BaseSampler): """A pseudo sampler that does not do sampling actually.""" def __init__(self, **kwargs): pass def _sample_pos(self, **kwargs): """Sample positive samples.""" raise NotImplementedError def _sample_neg(self, **kwargs): """Sample negative samples.""" raise NotImplementedError def sample(self, assign_result, masks, gt_masks, **kwargs): """Directly returns the positive and negative indices of samples. Args: assign_result (:obj:`AssignResult`): Assigned results masks (torch.Tensor): Bounding boxes gt_masks (torch.Tensor): Ground truth boxes Returns: :obj:`SamplingResult`: sampler results """ pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique() neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique() gt_flags = masks.new_zeros(masks.shape[0], dtype=torch.uint8) sampling_result = MaskSamplingResult(pos_inds, neg_inds, masks, gt_masks, assign_result, gt_flags) return sampling_result ================================================ FILE: dinov2/eval/segmentation_m2f/core/box/samplers/mask_sampling_result.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py import torch from .sampling_result import SamplingResult class MaskSamplingResult(SamplingResult): """Mask sampling result.""" def __init__(self, pos_inds, neg_inds, masks, gt_masks, assign_result, gt_flags): self.pos_inds = pos_inds self.neg_inds = neg_inds self.pos_masks = masks[pos_inds] self.neg_masks = masks[neg_inds] self.pos_is_gt = gt_flags[pos_inds] self.num_gts = gt_masks.shape[0] self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1 if gt_masks.numel() == 0: # hack for index error case assert self.pos_assigned_gt_inds.numel() == 0 self.pos_gt_masks = torch.empty_like(gt_masks) else: self.pos_gt_masks = gt_masks[self.pos_assigned_gt_inds, :] if assign_result.labels is not None: self.pos_gt_labels = assign_result.labels[pos_inds] else: self.pos_gt_labels = None @property def masks(self): """torch.Tensor: concatenated positive and negative boxes""" return torch.cat([self.pos_masks, self.neg_masks]) def __nice__(self): data = self.info.copy() data["pos_masks"] = data.pop("pos_masks").shape data["neg_masks"] = data.pop("neg_masks").shape parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())] body = " " + ",\n ".join(parts) return "{\n" + body + "\n}" @property def info(self): """Returns a dictionary of info about the object.""" return { "pos_inds": self.pos_inds, "neg_inds": self.neg_inds, "pos_masks": self.pos_masks, "neg_masks": self.neg_masks, "pos_is_gt": self.pos_is_gt, "num_gts": self.num_gts, "pos_assigned_gt_inds": self.pos_assigned_gt_inds, } ================================================ FILE: dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch class SamplingResult: """Bbox sampling result. Example: >>> # xdoctest: +IGNORE_WANT >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA >>> self = SamplingResult.random(rng=10) >>> print(f'self = {self}') self = """ def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result, gt_flags): self.pos_inds = pos_inds self.neg_inds = neg_inds self.pos_bboxes = bboxes[pos_inds] self.neg_bboxes = bboxes[neg_inds] self.pos_is_gt = gt_flags[pos_inds] self.num_gts = gt_bboxes.shape[0] self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1 if gt_bboxes.numel() == 0: # hack for index error case assert self.pos_assigned_gt_inds.numel() == 0 self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4) else: if len(gt_bboxes.shape) < 2: gt_bboxes = gt_bboxes.view(-1, 4) self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds.long(), :] if assign_result.labels is not None: self.pos_gt_labels = assign_result.labels[pos_inds] else: self.pos_gt_labels = None @property def bboxes(self): """torch.Tensor: concatenated positive and negative boxes""" return torch.cat([self.pos_bboxes, self.neg_bboxes]) def to(self, device): """Change the device of the data inplace. Example: >>> self = SamplingResult.random() >>> print(f'self = {self.to(None)}') >>> # xdoctest: +REQUIRES(--gpu) >>> print(f'self = {self.to(0)}') """ _dict = self.__dict__ for key, value in _dict.items(): if isinstance(value, torch.Tensor): _dict[key] = value.to(device) return self def __nice__(self): data = self.info.copy() data["pos_bboxes"] = data.pop("pos_bboxes").shape data["neg_bboxes"] = data.pop("neg_bboxes").shape parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())] body = " " + ",\n ".join(parts) return "{\n" + body + "\n}" @property def info(self): """Returns a dictionary of info about the object.""" return { "pos_inds": self.pos_inds, "neg_inds": self.neg_inds, "pos_bboxes": self.pos_bboxes, "neg_bboxes": self.neg_bboxes, "pos_is_gt": self.pos_is_gt, "num_gts": self.num_gts, "pos_assigned_gt_inds": self.pos_assigned_gt_inds, } @classmethod def random(cls, rng=None, **kwargs): """ Args: rng (None | int | numpy.random.RandomState): seed or state. kwargs (keyword arguments): - num_preds: number of predicted boxes - num_gts: number of true boxes - p_ignore (float): probability of a predicted box assigned to \ an ignored truth. - p_assigned (float): probability of a predicted box not being \ assigned. - p_use_label (float | bool): with labels or not. Returns: :obj:`SamplingResult`: Randomly generated sampling result. Example: >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA >>> self = SamplingResult.random() >>> print(self.__dict__) """ from mmdet.core.bbox import demodata from mmdet.core.bbox.assigners.assign_result import AssignResult from mmdet.core.bbox.samplers.random_sampler import RandomSampler rng = demodata.ensure_rng(rng) # make probabalistic? num = 32 pos_fraction = 0.5 neg_pos_ub = -1 assign_result = AssignResult.random(rng=rng, **kwargs) # Note we could just compute an assignment bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng) gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng) if rng.rand() > 0.2: # sometimes algorithms squeeze their data, be robust to that gt_bboxes = gt_bboxes.squeeze() bboxes = bboxes.squeeze() if assign_result.labels is None: gt_labels = None else: gt_labels = None if gt_labels is None: add_gt_as_proposals = False else: add_gt_as_proposals = True # make probabalistic? sampler = RandomSampler( num, pos_fraction, neg_pos_ub=neg_pos_ub, add_gt_as_proposals=add_gt_as_proposals, rng=rng ) self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) return self ================================================ FILE: dinov2/eval/segmentation_m2f/core/utils/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .dist_utils import reduce_mean from .misc import add_prefix, multi_apply ================================================ FILE: dinov2/eval/segmentation_m2f/core/utils/dist_utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch.distributed as dist def reduce_mean(tensor): """ "Obtain the mean of tensor on different GPUs.""" if not (dist.is_available() and dist.is_initialized()): return tensor tensor = tensor.clone() dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM) return tensor ================================================ FILE: dinov2/eval/segmentation_m2f/core/utils/misc.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from functools import partial def multi_apply(func, *args, **kwargs): """Apply function to a list of arguments. Note: This function applies the ``func`` to multiple inputs and map the multiple outputs of the ``func`` into different list. Each list contains the same type of outputs corresponding to different inputs. Args: func (Function): A function that will be applied to a list of arguments Returns: tuple(list): A tuple containing multiple list, each list contains \ a kind of returned results by the function """ pfunc = partial(func, **kwargs) if kwargs else func map_results = map(pfunc, *args) return tuple(map(list, zip(*map_results))) def add_prefix(inputs, prefix): """Add prefix for dict. Args: inputs (dict): The input dict with str keys. prefix (str): The prefix to add. Returns: dict: The dict with keys updated with ``prefix``. """ outputs = dict() for name, value in inputs.items(): outputs[f"{prefix}.{name}"] = value return outputs ================================================ FILE: dinov2/eval/segmentation_m2f/models/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .backbones import * # noqa: F403 from .builder import MASK_ASSIGNERS, MATCH_COST, TRANSFORMER, build_assigner, build_match_cost from .decode_heads import * # noqa: F403 from .losses import * # noqa: F403 from .plugins import * # noqa: F403 from .segmentors import * # noqa: F403 ================================================ FILE: dinov2/eval/segmentation_m2f/models/backbones/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .vit_adapter import ViTAdapter ================================================ FILE: dinov2/eval/segmentation_m2f/models/backbones/adapter_modules.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from functools import partial import torch import torch.nn as nn import torch.utils.checkpoint as cp from ...ops.modules import MSDeformAttn from .drop_path import DropPath def get_reference_points(spatial_shapes, device): reference_points_list = [] for lvl, (H_, W_) in enumerate(spatial_shapes): ref_y, ref_x = torch.meshgrid( torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device), ) ref_y = ref_y.reshape(-1)[None] / H_ ref_x = ref_x.reshape(-1)[None] / W_ ref = torch.stack((ref_x, ref_y), -1) reference_points_list.append(ref) reference_points = torch.cat(reference_points_list, 1) reference_points = reference_points[:, :, None] return reference_points def deform_inputs(x, patch_size): bs, c, h, w = x.shape spatial_shapes = torch.as_tensor( [(h // 8, w // 8), (h // 16, w // 16), (h // 32, w // 32)], dtype=torch.long, device=x.device ) level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) reference_points = get_reference_points([(h // patch_size, w // patch_size)], x.device) deform_inputs1 = [reference_points, spatial_shapes, level_start_index] spatial_shapes = torch.as_tensor([(h // patch_size, w // patch_size)], dtype=torch.long, device=x.device) level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) reference_points = get_reference_points([(h // 8, w // 8), (h // 16, w // 16), (h // 32, w // 32)], x.device) deform_inputs2 = [reference_points, spatial_shapes, level_start_index] return deform_inputs1, deform_inputs2 class ConvFFN(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): 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) 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 DWConv(nn.Module): def __init__(self, dim=768): super().__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 n = N // 21 x1 = x[:, 0 : 16 * n, :].transpose(1, 2).view(B, C, H * 2, W * 2).contiguous() x2 = x[:, 16 * n : 20 * n, :].transpose(1, 2).view(B, C, H, W).contiguous() x3 = x[:, 20 * n :, :].transpose(1, 2).view(B, C, H // 2, W // 2).contiguous() x1 = self.dwconv(x1).flatten(2).transpose(1, 2) x2 = self.dwconv(x2).flatten(2).transpose(1, 2) x3 = self.dwconv(x3).flatten(2).transpose(1, 2) x = torch.cat([x1, x2, x3], dim=1) return x class Extractor(nn.Module): def __init__( self, dim, num_heads=6, n_points=4, n_levels=1, deform_ratio=1.0, with_cffn=True, cffn_ratio=0.25, drop=0.0, drop_path=0.0, norm_layer=partial(nn.LayerNorm, eps=1e-6), with_cp=False, ): super().__init__() self.query_norm = norm_layer(dim) self.feat_norm = norm_layer(dim) self.attn = MSDeformAttn( d_model=dim, n_levels=n_levels, n_heads=num_heads, n_points=n_points, ratio=deform_ratio ) self.with_cffn = with_cffn self.with_cp = with_cp if with_cffn: self.ffn = ConvFFN(in_features=dim, hidden_features=int(dim * cffn_ratio), drop=drop) self.ffn_norm = norm_layer(dim) self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() def forward(self, query, reference_points, feat, spatial_shapes, level_start_index, H, W): def _inner_forward(query, feat): attn = self.attn( self.query_norm(query), reference_points, self.feat_norm(feat), spatial_shapes, level_start_index, None ) query = query + attn if self.with_cffn: query = query + self.drop_path(self.ffn(self.ffn_norm(query), H, W)) return query if self.with_cp and query.requires_grad: query = cp.checkpoint(_inner_forward, query, feat) else: query = _inner_forward(query, feat) return query class Injector(nn.Module): def __init__( self, dim, num_heads=6, n_points=4, n_levels=1, deform_ratio=1.0, norm_layer=partial(nn.LayerNorm, eps=1e-6), init_values=0.0, with_cp=False, ): super().__init__() self.with_cp = with_cp self.query_norm = norm_layer(dim) self.feat_norm = norm_layer(dim) self.attn = MSDeformAttn( d_model=dim, n_levels=n_levels, n_heads=num_heads, n_points=n_points, ratio=deform_ratio ) self.gamma = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) def forward(self, query, reference_points, feat, spatial_shapes, level_start_index): def _inner_forward(query, feat): attn = self.attn( self.query_norm(query), reference_points, self.feat_norm(feat), spatial_shapes, level_start_index, None ) return query + self.gamma * attn if self.with_cp and query.requires_grad: query = cp.checkpoint(_inner_forward, query, feat) else: query = _inner_forward(query, feat) return query class InteractionBlock(nn.Module): def __init__( self, dim, num_heads=6, n_points=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), drop=0.0, drop_path=0.0, with_cffn=True, cffn_ratio=0.25, init_values=0.0, deform_ratio=1.0, extra_extractor=False, with_cp=False, ): super().__init__() self.injector = Injector( dim=dim, n_levels=3, num_heads=num_heads, init_values=init_values, n_points=n_points, norm_layer=norm_layer, deform_ratio=deform_ratio, with_cp=with_cp, ) self.extractor = Extractor( dim=dim, n_levels=1, num_heads=num_heads, n_points=n_points, norm_layer=norm_layer, deform_ratio=deform_ratio, with_cffn=with_cffn, cffn_ratio=cffn_ratio, drop=drop, drop_path=drop_path, with_cp=with_cp, ) if extra_extractor: self.extra_extractors = nn.Sequential( *[ Extractor( dim=dim, num_heads=num_heads, n_points=n_points, norm_layer=norm_layer, with_cffn=with_cffn, cffn_ratio=cffn_ratio, deform_ratio=deform_ratio, drop=drop, drop_path=drop_path, with_cp=with_cp, ) for _ in range(2) ] ) else: self.extra_extractors = None def forward(self, x, c, blocks, deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks): x = self.injector( query=x, reference_points=deform_inputs1[0], feat=c, spatial_shapes=deform_inputs1[1], level_start_index=deform_inputs1[2], ) for idx, blk in enumerate(blocks): x = blk(x, H_toks, W_toks) c = self.extractor( query=c, reference_points=deform_inputs2[0], feat=x, spatial_shapes=deform_inputs2[1], level_start_index=deform_inputs2[2], H=H_c, W=W_c, ) if self.extra_extractors is not None: for extractor in self.extra_extractors: c = extractor( query=c, reference_points=deform_inputs2[0], feat=x, spatial_shapes=deform_inputs2[1], level_start_index=deform_inputs2[2], H=H_c, W=W_c, ) return x, c class InteractionBlockWithCls(nn.Module): def __init__( self, dim, num_heads=6, n_points=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), drop=0.0, drop_path=0.0, with_cffn=True, cffn_ratio=0.25, init_values=0.0, deform_ratio=1.0, extra_extractor=False, with_cp=False, ): super().__init__() self.injector = Injector( dim=dim, n_levels=3, num_heads=num_heads, init_values=init_values, n_points=n_points, norm_layer=norm_layer, deform_ratio=deform_ratio, with_cp=with_cp, ) self.extractor = Extractor( dim=dim, n_levels=1, num_heads=num_heads, n_points=n_points, norm_layer=norm_layer, deform_ratio=deform_ratio, with_cffn=with_cffn, cffn_ratio=cffn_ratio, drop=drop, drop_path=drop_path, with_cp=with_cp, ) if extra_extractor: self.extra_extractors = nn.Sequential( *[ Extractor( dim=dim, num_heads=num_heads, n_points=n_points, norm_layer=norm_layer, with_cffn=with_cffn, cffn_ratio=cffn_ratio, deform_ratio=deform_ratio, drop=drop, drop_path=drop_path, with_cp=with_cp, ) for _ in range(2) ] ) else: self.extra_extractors = None def forward(self, x, c, cls, blocks, deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks): x = self.injector( query=x, reference_points=deform_inputs1[0], feat=c, spatial_shapes=deform_inputs1[1], level_start_index=deform_inputs1[2], ) x = torch.cat((cls, x), dim=1) for idx, blk in enumerate(blocks): x = blk(x, H_toks, W_toks) cls, x = ( x[ :, :1, ], x[ :, 1:, ], ) c = self.extractor( query=c, reference_points=deform_inputs2[0], feat=x, spatial_shapes=deform_inputs2[1], level_start_index=deform_inputs2[2], H=H_c, W=W_c, ) if self.extra_extractors is not None: for extractor in self.extra_extractors: c = extractor( query=c, reference_points=deform_inputs2[0], feat=x, spatial_shapes=deform_inputs2[1], level_start_index=deform_inputs2[2], H=H_c, W=W_c, ) return x, c, cls class SpatialPriorModule(nn.Module): def __init__(self, inplanes=64, embed_dim=384, with_cp=False): super().__init__() self.with_cp = with_cp self.stem = nn.Sequential( *[ nn.Conv2d(3, inplanes, kernel_size=3, stride=2, padding=1, bias=False), nn.SyncBatchNorm(inplanes), nn.ReLU(inplace=True), nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1, bias=False), nn.SyncBatchNorm(inplanes), nn.ReLU(inplace=True), nn.Conv2d(inplanes, inplanes, kernel_size=3, stride=1, padding=1, bias=False), nn.SyncBatchNorm(inplanes), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1), ] ) self.conv2 = nn.Sequential( *[ nn.Conv2d(inplanes, 2 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), nn.SyncBatchNorm(2 * inplanes), nn.ReLU(inplace=True), ] ) self.conv3 = nn.Sequential( *[ nn.Conv2d(2 * inplanes, 4 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), nn.SyncBatchNorm(4 * inplanes), nn.ReLU(inplace=True), ] ) self.conv4 = nn.Sequential( *[ nn.Conv2d(4 * inplanes, 4 * inplanes, kernel_size=3, stride=2, padding=1, bias=False), nn.SyncBatchNorm(4 * inplanes), nn.ReLU(inplace=True), ] ) self.fc1 = nn.Conv2d(inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) self.fc2 = nn.Conv2d(2 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) self.fc3 = nn.Conv2d(4 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) self.fc4 = nn.Conv2d(4 * inplanes, embed_dim, kernel_size=1, stride=1, padding=0, bias=True) def forward(self, x): def _inner_forward(x): c1 = self.stem(x) c2 = self.conv2(c1) c3 = self.conv3(c2) c4 = self.conv4(c3) c1 = self.fc1(c1) c2 = self.fc2(c2) c3 = self.fc3(c3) c4 = self.fc4(c4) bs, dim, _, _ = c1.shape # c1 = c1.view(bs, dim, -1).transpose(1, 2) # 4s c2 = c2.view(bs, dim, -1).transpose(1, 2) # 8s c3 = c3.view(bs, dim, -1).transpose(1, 2) # 16s c4 = c4.view(bs, dim, -1).transpose(1, 2) # 32s return c1, c2, c3, c4 if self.with_cp and x.requires_grad: outs = cp.checkpoint(_inner_forward, x) else: outs = _inner_forward(x) return outs ================================================ FILE: dinov2/eval/segmentation_m2f/models/backbones/drop_path.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py # https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py from torch import nn def drop_path(x, drop_prob: float = 0.0, training: bool = False): if drop_prob == 0.0 or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0: random_tensor.div_(keep_prob) return x * random_tensor class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" def __init__(self, drop_prob: float = 0.0): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) ================================================ FILE: dinov2/eval/segmentation_m2f/models/backbones/vit.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. """Vision Transformer (ViT) in PyTorch. A PyTorch implement of Vision Transformers as described in: 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929 `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers` - https://arxiv.org/abs/2106.10270 The official jax code is released and available at https://github.com/google-research/vision_transformer DeiT model defs and weights from https://github.com/facebookresearch/deit, paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 Acknowledgments: * The paper authors for releasing code and weights, thanks! * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out for some einops/einsum fun * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT * Bert reference code checks against Huggingface Transformers and Tensorflow Bert Hacked together by / Copyright 2021 Ross Wightman """ import logging import math from functools import partial from itertools import repeat from typing import Callable, Optional import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as cp from mmcv.runner import BaseModule, load_checkpoint from mmseg.ops import resize from mmseg.utils import get_root_logger from torch import Tensor from .drop_path import DropPath def to_2tuple(x): return tuple(repeat(x, 2)) class Mlp(nn.Module): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = nn.GELU, drop: float = 0.0, bias: bool = True, ) -> None: super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) self.drop = nn.Dropout(drop) def forward(self, x: Tensor) -> Tensor: x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class SwiGLUFFN(nn.Module): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = None, drop: float = 0.0, ) -> None: super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features swiglu_hidden_features = int(2 * hidden_features / 3) align_as = 8 swiglu_hidden_features = (swiglu_hidden_features + align_as - 1) // align_as * align_as self.w1 = nn.Linear(in_features, swiglu_hidden_features) self.w2 = nn.Linear(in_features, swiglu_hidden_features) self.w3 = nn.Linear(swiglu_hidden_features, out_features) def forward(self, x: Tensor) -> Tensor: x1 = self.w1(x) x2 = self.w2(x) hidden = F.silu(x1) * x2 return self.w3(hidden) class PatchEmbed(nn.Module): """2D Image to Patch Embedding.""" def __init__( self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, bias=True ): 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.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) self.num_patches = self.grid_size[0] * self.grid_size[1] self.flatten = flatten self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): x = self.proj(x) _, _, H, W = x.shape if self.flatten: x = x.flatten(2).transpose(1, 2) # BCHW -> BNC x = self.norm(x) return x, H, W class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, H, W): 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.unbind(0) # make torchscript happy (cannot use tensor as tuple) 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 MemEffAttention(nn.Module): def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, attn_drop: float = 0.0, proj_drop: float = 0.0, ) -> None: super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x: Tensor, H, W) -> Tensor: from xformers.ops import memory_efficient_attention, unbind B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) q, k, v = unbind(qkv, 2) x = memory_efficient_attention(q, k, v) x = x.reshape([B, N, C]) x = self.proj(x) x = self.proj_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 WindowedAttention(nn.Module): def __init__( self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0, window_size=14, pad_mode="constant" ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.window_size = window_size self.pad_mode = pad_mode def forward(self, x, H, W): B, N, C = x.shape N_ = self.window_size * self.window_size H_ = math.ceil(H / self.window_size) * self.window_size W_ = math.ceil(W / self.window_size) * self.window_size qkv = self.qkv(x) # [B, N, C] qkv = qkv.transpose(1, 2).reshape(B, C * 3, H, W) # [B, C, H, W] qkv = F.pad(qkv, [0, W_ - W, 0, H_ - H], mode=self.pad_mode) qkv = F.unfold( qkv, kernel_size=(self.window_size, self.window_size), stride=(self.window_size, self.window_size) ) B, C_kw_kw, L = qkv.shape # L - the num of windows qkv = qkv.reshape(B, C * 3, N_, L).permute(0, 3, 2, 1) # [B, L, N_, C] qkv = qkv.reshape(B, L, N_, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5) q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) # q,k,v [B, L, num_head, N_, C/num_head] attn = (q @ k.transpose(-2, -1)) * self.scale # [B, L, num_head, N_, N_] # if self.mask: # attn = attn * mask attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) # [B, L, num_head, N_, N_] # attn @ v = [B, L, num_head, N_, C/num_head] x = (attn @ v).permute(0, 2, 4, 3, 1).reshape(B, C_kw_kw // 3, L) x = F.fold( x, output_size=(H_, W_), kernel_size=(self.window_size, self.window_size), stride=(self.window_size, self.window_size), ) # [B, C, H_, W_] x = x[:, :, :H, :W].reshape(B, C, N).transpose(-1, -2) x = self.proj(x) x = self.proj_drop(x) return x # class WindowedAttention(nn.Module): # def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., window_size=14, pad_mode="constant"): # super().__init__() # self.num_heads = num_heads # head_dim = dim // num_heads # self.scale = head_dim ** -0.5 # # self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) # self.attn_drop = nn.Dropout(attn_drop) # self.proj = nn.Linear(dim, dim) # self.proj_drop = nn.Dropout(proj_drop) # self.window_size = window_size # self.pad_mode = pad_mode # # def forward(self, x, H, W): # B, N, C = x.shape # # N_ = self.window_size * self.window_size # H_ = math.ceil(H / self.window_size) * self.window_size # W_ = math.ceil(W / self.window_size) * self.window_size # x = x.view(B, H, W, C) # x = F.pad(x, [0, 0, 0, W_ - W, 0, H_- H], mode=self.pad_mode) # # x = window_partition(x, window_size=self.window_size)# nW*B, window_size, window_size, C # x = x.view(-1, N_, C) # # qkv = self.qkv(x).view(-1, N_, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) # attn = (q @ k.transpose(-2, -1)) * self.scale # [B, L, num_head, N_, N_] # attn = attn.softmax(dim=-1) # attn = self.attn_drop(attn) # [B, L, num_head, N_, N_] # x = (attn @ v).transpose(1, 2).reshape(-1, self.window_size, self.window_size, C) # # x = window_reverse(x, self.window_size, H_, W_) # x = x[:, :H, :W, :].reshape(B, N, C).contiguous() # x = self.proj(x) # x = self.proj_drop(x) # return x class Block(nn.Module): def __init__( self, dim, num_heads, mlp_ratio=4.0, qkv_bias=False, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm, windowed=False, window_size=14, pad_mode="constant", layer_scale=False, with_cp=False, ffn_layer=Mlp, memeff=False, ): super().__init__() self.with_cp = with_cp self.norm1 = norm_layer(dim) if windowed: self.attn = WindowedAttention( dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, window_size=window_size, pad_mode=pad_mode, ) elif memeff: self.attn = MemEffAttention( dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop ) else: self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) # 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 > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = ffn_layer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) self.layer_scale = layer_scale if layer_scale: self.gamma1 = nn.Parameter(torch.ones((dim)), requires_grad=True) self.gamma2 = nn.Parameter(torch.ones((dim)), requires_grad=True) def forward(self, x, H, W): def _inner_forward(x): if self.layer_scale: x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x), H, W)) x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x))) else: x = x + self.drop_path(self.attn(self.norm1(x), H, W)) x = x + self.drop_path(self.mlp(self.norm2(x))) return x if self.with_cp and x.requires_grad: x = cp.checkpoint(_inner_forward, x) else: x = _inner_forward(x) return x class TIMMVisionTransformer(BaseModule): """Vision Transformer. A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 Includes distillation token & head support for `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877 """ def __init__( self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, layer_scale=True, embed_layer=PatchEmbed, norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU, window_attn=False, window_size=14, pretrained=None, with_cp=False, pre_norm=False, ffn_type="mlp", memeff=False, ): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer pretrained: (str): pretrained path """ super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 1 norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU self.norm_layer = norm_layer self.act_layer = act_layer self.pretrain_size = img_size self.drop_path_rate = drop_path_rate self.drop_rate = drop_rate self.patch_size = patch_size window_attn = [window_attn] * depth if not isinstance(window_attn, list) else window_attn window_size = [window_size] * depth if not isinstance(window_size, list) else window_size logging.info("window attention:", window_attn) logging.info("window size:", window_size) logging.info("layer scale:", layer_scale) self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, bias=not pre_norm ) num_patches = self.patch_embed.num_patches self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) ffn_types = {"mlp": Mlp, "swiglu": SwiGLUFFN} dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.Sequential( *[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, windowed=window_attn[i], window_size=window_size[i], layer_scale=layer_scale, with_cp=with_cp, ffn_layer=ffn_types[ffn_type], memeff=memeff, ) for i in range(depth) ] ) # self.norm = norm_layer(embed_dim) self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) # For CLIP if pre_norm: norm_pre = norm_layer(embed_dim) self.norm_pre = norm_pre else: self.norm_pre = nn.Identity() self.init_weights(pretrained) def init_weights(self, pretrained=None): if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, map_location="cpu", strict=False, logger=logger) def forward_features(self, x): x, H, W = self.patch_embed(x) cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks x = torch.cat((cls_token, x), dim=1) x = self.pos_drop(x + self.pos_embed) # For CLIP x = self.norm_pre(x) for blk in self.blocks: x = blk(x, H, W) x = self.norm(x) return x def forward(self, x): x = self.forward_features(x) return x @staticmethod def resize_pos_embed(pos_embed, input_shpae, pos_shape, mode): """Resize pos_embed weights. Resize pos_embed using bicubic interpolate method. Args: pos_embed (torch.Tensor): Position embedding weights. input_shpae (tuple): Tuple for (downsampled input image height, downsampled input image width). pos_shape (tuple): The resolution of downsampled origin training image. mode (str): Algorithm used for upsampling: ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` | ``'trilinear'``. Default: ``'nearest'`` Return: torch.Tensor: The resized pos_embed of shape [B, L_new, C] """ assert pos_embed.ndim == 3, "shape of pos_embed must be [B, L, C]" pos_h, pos_w = pos_shape # keep dim for easy deployment cls_token_weight = pos_embed[:, 0:1] pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w) :] pos_embed_weight = pos_embed_weight.reshape(1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2) pos_embed_weight = resize(pos_embed_weight, size=input_shpae, align_corners=False, mode=mode) pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2) pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1) return pos_embed ================================================ FILE: dinov2/eval/segmentation_m2f/models/backbones/vit_adapter.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import math import torch import torch.nn as nn import torch.nn.functional as F from mmseg.models.builder import BACKBONES from torch.nn.init import normal_ from ...ops.modules import MSDeformAttn from .adapter_modules import InteractionBlock, InteractionBlockWithCls, SpatialPriorModule, deform_inputs from .vit import TIMMVisionTransformer @BACKBONES.register_module() class ViTAdapter(TIMMVisionTransformer): def __init__( self, pretrain_size=224, num_heads=12, conv_inplane=64, n_points=4, deform_num_heads=6, init_values=0.0, interaction_indexes=None, with_cffn=True, cffn_ratio=0.25, deform_ratio=1.0, add_vit_feature=True, pretrained=None, use_extra_extractor=True, freeze_vit=False, use_cls=True, with_cp=False, *args, **kwargs ): super().__init__(num_heads=num_heads, pretrained=pretrained, with_cp=with_cp, *args, **kwargs) if freeze_vit: for param in self.parameters(): param.requires_grad = False # self.num_classes = 80 self.use_cls = use_cls if not self.use_cls: self.cls_token = None self.num_block = len(self.blocks) self.pretrain_size = (pretrain_size, pretrain_size) self.interaction_indexes = interaction_indexes self.add_vit_feature = add_vit_feature embed_dim = self.embed_dim block_fn = InteractionBlockWithCls if use_cls else InteractionBlock self.level_embed = nn.Parameter(torch.zeros(3, embed_dim)) self.spm = SpatialPriorModule(inplanes=conv_inplane, embed_dim=embed_dim, with_cp=False) self.interactions = nn.Sequential( *[ block_fn( dim=embed_dim, num_heads=deform_num_heads, n_points=n_points, init_values=init_values, drop_path=self.drop_path_rate, norm_layer=self.norm_layer, with_cffn=with_cffn, cffn_ratio=cffn_ratio, deform_ratio=deform_ratio, extra_extractor=((True if i == len(interaction_indexes) - 1 else False) and use_extra_extractor), with_cp=with_cp, ) for i in range(len(interaction_indexes)) ] ) self.up = nn.ConvTranspose2d(embed_dim, embed_dim, 2, 2) self.norm1 = nn.SyncBatchNorm(embed_dim) self.norm2 = nn.SyncBatchNorm(embed_dim) self.norm3 = nn.SyncBatchNorm(embed_dim) self.norm4 = nn.SyncBatchNorm(embed_dim) self.up.apply(self._init_weights) self.spm.apply(self._init_weights) self.interactions.apply(self._init_weights) self.apply(self._init_deform_weights) normal_(self.level_embed) def _init_weights(self, m): if isinstance(m, nn.Linear): torch.nn.init.trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d): 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 _get_pos_embed(self, pos_embed, H, W): pos_embed = pos_embed.reshape( 1, self.pretrain_size[0] // self.patch_size, self.pretrain_size[1] // self.patch_size, -1 ).permute(0, 3, 1, 2) pos_embed = ( F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False) .reshape(1, -1, H * W) .permute(0, 2, 1) ) return pos_embed def _init_deform_weights(self, m): if isinstance(m, MSDeformAttn): m._reset_parameters() def _add_level_embed(self, c2, c3, c4): c2 = c2 + self.level_embed[0] c3 = c3 + self.level_embed[1] c4 = c4 + self.level_embed[2] return c2, c3, c4 def forward(self, x): deform_inputs1, deform_inputs2 = deform_inputs(x, self.patch_size) # SPM forward c1, c2, c3, c4 = self.spm(x) c2, c3, c4 = self._add_level_embed(c2, c3, c4) c = torch.cat([c2, c3, c4], dim=1) # Patch Embedding forward H_c, W_c = x.shape[2] // 16, x.shape[3] // 16 x, H_toks, W_toks = self.patch_embed(x) # print("H_toks, W_toks =", H_toks, W_toks) bs, n, dim = x.shape pos_embed = self._get_pos_embed(self.pos_embed[:, 1:], H_toks, W_toks) if self.use_cls: cls_token = self.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks x = torch.cat((cls_token, x), dim=1) pos_embed = torch.cat((self.pos_embed[:, :1], pos_embed), dim=1) x = self.pos_drop(x + pos_embed) # For CLIP x = self.norm_pre(x) # Interaction if self.use_cls: cls, x = ( x[ :, :1, ], x[ :, 1:, ], ) outs = list() for i, layer in enumerate(self.interactions): indexes = self.interaction_indexes[i] if self.use_cls: x, c, cls = layer( x, c, cls, self.blocks[indexes[0] : indexes[-1] + 1], deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks, ) else: x, c = layer( x, c, self.blocks[indexes[0] : indexes[-1] + 1], deform_inputs1, deform_inputs2, H_c, W_c, H_toks, W_toks, ) outs.append(x.transpose(1, 2).view(bs, dim, H_toks, W_toks).contiguous()) # Split & Reshape c2 = c[:, 0 : c2.size(1), :] c3 = c[:, c2.size(1) : c2.size(1) + c3.size(1), :] c4 = c[:, c2.size(1) + c3.size(1) :, :] c2 = c2.transpose(1, 2).view(bs, dim, H_c * 2, W_c * 2).contiguous() c3 = c3.transpose(1, 2).view(bs, dim, H_c, W_c).contiguous() c4 = c4.transpose(1, 2).view(bs, dim, H_c // 2, W_c // 2).contiguous() c1 = self.up(c2) + c1 if self.add_vit_feature: x1, x2, x3, x4 = outs x1 = F.interpolate(x1, size=(4 * H_c, 4 * W_c), mode="bilinear", align_corners=False) x2 = F.interpolate(x2, size=(2 * H_c, 2 * W_c), mode="bilinear", align_corners=False) x3 = F.interpolate(x3, size=(1 * H_c, 1 * W_c), mode="bilinear", align_corners=False) x4 = F.interpolate(x4, size=(H_c // 2, W_c // 2), mode="bilinear", align_corners=False) # print(c1.shape, c2.shape, c3.shape, c4.shape, x1.shape, x2.shape, x3.shape, x4.shape, H_c, H_toks) c1, c2, c3, c4 = c1 + x1, c2 + x2, c3 + x3, c4 + x4 # Final Norm f1 = self.norm1(c1) f2 = self.norm2(c2) f3 = self.norm3(c3) f4 = self.norm4(c4) return [f1, f2, f3, f4] ================================================ FILE: dinov2/eval/segmentation_m2f/models/builder.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from mmcv.utils import Registry TRANSFORMER = Registry("Transformer") MASK_ASSIGNERS = Registry("mask_assigner") MATCH_COST = Registry("match_cost") def build_match_cost(cfg): """Build Match Cost.""" return MATCH_COST.build(cfg) def build_assigner(cfg): """Build Assigner.""" return MASK_ASSIGNERS.build(cfg) def build_transformer(cfg): """Build Transformer.""" return TRANSFORMER.build(cfg) ================================================ FILE: dinov2/eval/segmentation_m2f/models/decode_heads/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .mask2former_head import Mask2FormerHead ================================================ FILE: dinov2/eval/segmentation_m2f/models/decode_heads/mask2former_head.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import copy import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import Conv2d, build_plugin_layer, caffe2_xavier_init from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer_sequence from mmcv.ops import point_sample from mmcv.runner import ModuleList, force_fp32 from mmseg.models.builder import HEADS, build_loss from mmseg.models.decode_heads.decode_head import BaseDecodeHead from ...core import build_sampler, multi_apply, reduce_mean from ..builder import build_assigner from ..utils import get_uncertain_point_coords_with_randomness @HEADS.register_module() class Mask2FormerHead(BaseDecodeHead): """Implements the Mask2Former head. See `Masked-attention Mask Transformer for Universal Image Segmentation `_ for details. Args: in_channels (list[int]): Number of channels in the input feature map. feat_channels (int): Number of channels for features. out_channels (int): Number of channels for output. num_things_classes (int): Number of things. num_stuff_classes (int): Number of stuff. num_queries (int): Number of query in Transformer decoder. pixel_decoder (:obj:`mmcv.ConfigDict` | dict): Config for pixel decoder. Defaults to None. enforce_decoder_input_project (bool, optional): Whether to add a layer to change the embed_dim of tranformer encoder in pixel decoder to the embed_dim of transformer decoder. Defaults to False. transformer_decoder (:obj:`mmcv.ConfigDict` | dict): Config for transformer decoder. Defaults to None. positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for transformer decoder position encoding. Defaults to None. loss_cls (:obj:`mmcv.ConfigDict` | dict): Config of the classification loss. Defaults to None. loss_mask (:obj:`mmcv.ConfigDict` | dict): Config of the mask loss. Defaults to None. loss_dice (:obj:`mmcv.ConfigDict` | dict): Config of the dice loss. Defaults to None. train_cfg (:obj:`mmcv.ConfigDict` | dict): Training config of Mask2Former head. test_cfg (:obj:`mmcv.ConfigDict` | dict): Testing config of Mask2Former head. init_cfg (dict or list[dict], optional): Initialization config dict. Defaults to None. """ def __init__( self, in_channels, feat_channels, out_channels, num_things_classes=80, num_stuff_classes=53, num_queries=100, num_transformer_feat_level=3, pixel_decoder=None, enforce_decoder_input_project=False, transformer_decoder=None, positional_encoding=None, loss_cls=None, loss_mask=None, loss_dice=None, train_cfg=None, test_cfg=None, init_cfg=None, **kwargs, ): super(Mask2FormerHead, self).__init__( in_channels=in_channels, channels=feat_channels, num_classes=(num_things_classes + num_stuff_classes), init_cfg=init_cfg, input_transform="multiple_select", **kwargs, ) self.num_things_classes = num_things_classes self.num_stuff_classes = num_stuff_classes self.num_classes = self.num_things_classes + self.num_stuff_classes self.num_queries = num_queries self.num_transformer_feat_level = num_transformer_feat_level self.num_heads = transformer_decoder.transformerlayers.attn_cfgs.num_heads self.num_transformer_decoder_layers = transformer_decoder.num_layers assert pixel_decoder.encoder.transformerlayers.attn_cfgs.num_levels == num_transformer_feat_level pixel_decoder_ = copy.deepcopy(pixel_decoder) pixel_decoder_.update(in_channels=in_channels, feat_channels=feat_channels, out_channels=out_channels) self.pixel_decoder = build_plugin_layer(pixel_decoder_)[1] self.transformer_decoder = build_transformer_layer_sequence(transformer_decoder) self.decoder_embed_dims = self.transformer_decoder.embed_dims self.decoder_input_projs = ModuleList() # from low resolution to high resolution for _ in range(num_transformer_feat_level): if self.decoder_embed_dims != feat_channels or enforce_decoder_input_project: self.decoder_input_projs.append(Conv2d(feat_channels, self.decoder_embed_dims, kernel_size=1)) else: self.decoder_input_projs.append(nn.Identity()) self.decoder_positional_encoding = build_positional_encoding(positional_encoding) self.query_embed = nn.Embedding(self.num_queries, feat_channels) self.query_feat = nn.Embedding(self.num_queries, feat_channels) # from low resolution to high resolution self.level_embed = nn.Embedding(self.num_transformer_feat_level, feat_channels) self.cls_embed = nn.Linear(feat_channels, self.num_classes + 1) self.mask_embed = nn.Sequential( nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True), nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True), nn.Linear(feat_channels, out_channels), ) self.conv_seg = None # fix a bug here (conv_seg is not used) self.test_cfg = test_cfg self.train_cfg = train_cfg if train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) self.sampler = build_sampler(self.train_cfg.sampler, context=self) self.num_points = self.train_cfg.get("num_points", 12544) self.oversample_ratio = self.train_cfg.get("oversample_ratio", 3.0) self.importance_sample_ratio = self.train_cfg.get("importance_sample_ratio", 0.75) self.class_weight = loss_cls.class_weight self.loss_cls = build_loss(loss_cls) self.loss_mask = build_loss(loss_mask) self.loss_dice = build_loss(loss_dice) def init_weights(self): for m in self.decoder_input_projs: if isinstance(m, Conv2d): caffe2_xavier_init(m, bias=0) self.pixel_decoder.init_weights() for p in self.transformer_decoder.parameters(): if p.dim() > 1: nn.init.xavier_normal_(p) def get_targets(self, cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas): """Compute classification and mask targets for all images for a decoder layer. Args: cls_scores_list (list[Tensor]): Mask score logits from a single decoder layer for all images. Each with shape [num_queries, cls_out_channels]. mask_preds_list (list[Tensor]): Mask logits from a single decoder layer for all images. Each with shape [num_queries, h, w]. gt_labels_list (list[Tensor]): Ground truth class indices for all images. Each with shape (n, ), n is the sum of number of stuff type and number of instance in a image. gt_masks_list (list[Tensor]): Ground truth mask for each image, each with shape (n, h, w). img_metas (list[dict]): List of image meta information. Returns: tuple[list[Tensor]]: a tuple containing the following targets. - labels_list (list[Tensor]): Labels of all images. Each with shape [num_queries, ]. - label_weights_list (list[Tensor]): Label weights of all images.Each with shape [num_queries, ]. - mask_targets_list (list[Tensor]): Mask targets of all images. Each with shape [num_queries, h, w]. - mask_weights_list (list[Tensor]): Mask weights of all images. Each with shape [num_queries, ]. - num_total_pos (int): Number of positive samples in all images. - num_total_neg (int): Number of negative samples in all images. """ ( labels_list, label_weights_list, mask_targets_list, mask_weights_list, pos_inds_list, neg_inds_list, ) = multi_apply( self._get_target_single, cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas ) num_total_pos = sum((inds.numel() for inds in pos_inds_list)) num_total_neg = sum((inds.numel() for inds in neg_inds_list)) return (labels_list, label_weights_list, mask_targets_list, mask_weights_list, num_total_pos, num_total_neg) def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks, img_metas): """Compute classification and mask targets for one image. Args: cls_score (Tensor): Mask score logits from a single decoder layer for one image. Shape (num_queries, cls_out_channels). mask_pred (Tensor): Mask logits for a single decoder layer for one image. Shape (num_queries, h, w). gt_labels (Tensor): Ground truth class indices for one image with shape (num_gts, ). gt_masks (Tensor): Ground truth mask for each image, each with shape (num_gts, h, w). img_metas (dict): Image informtation. Returns: tuple[Tensor]: A tuple containing the following for one image. - labels (Tensor): Labels of each image. \ shape (num_queries, ). - label_weights (Tensor): Label weights of each image. \ shape (num_queries, ). - mask_targets (Tensor): Mask targets of each image. \ shape (num_queries, h, w). - mask_weights (Tensor): Mask weights of each image. \ shape (num_queries, ). - pos_inds (Tensor): Sampled positive indices for each \ image. - neg_inds (Tensor): Sampled negative indices for each \ image. """ # sample points num_queries = cls_score.shape[0] num_gts = gt_labels.shape[0] point_coords = torch.rand((1, self.num_points, 2), device=cls_score.device) # shape (num_queries, num_points) mask_points_pred = point_sample(mask_pred.unsqueeze(1), point_coords.repeat(num_queries, 1, 1)).squeeze(1) # shape (num_gts, num_points) gt_points_masks = point_sample(gt_masks.unsqueeze(1).float(), point_coords.repeat(num_gts, 1, 1)).squeeze(1) # assign and sample assign_result = self.assigner.assign(cls_score, mask_points_pred, gt_labels, gt_points_masks, img_metas) sampling_result = self.sampler.sample(assign_result, mask_pred, gt_masks) pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds # label target labels = gt_labels.new_full((self.num_queries,), self.num_classes, dtype=torch.long) labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds] label_weights = gt_labels.new_ones((self.num_queries,)) # mask target mask_targets = gt_masks[sampling_result.pos_assigned_gt_inds] mask_weights = mask_pred.new_zeros((self.num_queries,)) mask_weights[pos_inds] = 1.0 return (labels, label_weights, mask_targets, mask_weights, pos_inds, neg_inds) def loss_single(self, cls_scores, mask_preds, gt_labels_list, gt_masks_list, img_metas): """Loss function for outputs from a single decoder layer. Args: cls_scores (Tensor): Mask score logits from a single decoder layer for all images. Shape (batch_size, num_queries, cls_out_channels). Note `cls_out_channels` should includes background. mask_preds (Tensor): Mask logits for a pixel decoder for all images. Shape (batch_size, num_queries, h, w). gt_labels_list (list[Tensor]): Ground truth class indices for each image, each with shape (num_gts, ). gt_masks_list (list[Tensor]): Ground truth mask for each image, each with shape (num_gts, h, w). img_metas (list[dict]): List of image meta information. Returns: tuple[Tensor]: Loss components for outputs from a single \ decoder layer. """ num_imgs = cls_scores.size(0) cls_scores_list = [cls_scores[i] for i in range(num_imgs)] mask_preds_list = [mask_preds[i] for i in range(num_imgs)] ( labels_list, label_weights_list, mask_targets_list, mask_weights_list, num_total_pos, num_total_neg, ) = self.get_targets(cls_scores_list, mask_preds_list, gt_labels_list, gt_masks_list, img_metas) # shape (batch_size, num_queries) labels = torch.stack(labels_list, dim=0) # shape (batch_size, num_queries) label_weights = torch.stack(label_weights_list, dim=0) # shape (num_total_gts, h, w) mask_targets = torch.cat(mask_targets_list, dim=0) # shape (batch_size, num_queries) mask_weights = torch.stack(mask_weights_list, dim=0) # classfication loss # shape (batch_size * num_queries, ) cls_scores = cls_scores.flatten(0, 1) labels = labels.flatten(0, 1) label_weights = label_weights.flatten(0, 1) class_weight = cls_scores.new_tensor(self.class_weight) loss_cls = self.loss_cls(cls_scores, labels, label_weights, avg_factor=class_weight[labels].sum()) num_total_masks = reduce_mean(cls_scores.new_tensor([num_total_pos])) num_total_masks = max(num_total_masks, 1) # extract positive ones # shape (batch_size, num_queries, h, w) -> (num_total_gts, h, w) mask_preds = mask_preds[mask_weights > 0] if mask_targets.shape[0] == 0: # zero match loss_dice = mask_preds.sum() loss_mask = mask_preds.sum() return loss_cls, loss_mask, loss_dice with torch.no_grad(): points_coords = get_uncertain_point_coords_with_randomness( mask_preds.unsqueeze(1), None, self.num_points, self.oversample_ratio, self.importance_sample_ratio ) # shape (num_total_gts, h, w) -> (num_total_gts, num_points) mask_point_targets = point_sample(mask_targets.unsqueeze(1).float(), points_coords).squeeze(1) # shape (num_queries, h, w) -> (num_queries, num_points) mask_point_preds = point_sample(mask_preds.unsqueeze(1), points_coords).squeeze(1) # dice loss loss_dice = self.loss_dice(mask_point_preds, mask_point_targets, avg_factor=num_total_masks) # mask loss # shape (num_queries, num_points) -> (num_queries * num_points, ) mask_point_preds = mask_point_preds.reshape(-1, 1) # shape (num_total_gts, num_points) -> (num_total_gts * num_points, ) mask_point_targets = mask_point_targets.reshape(-1) loss_mask = self.loss_mask(mask_point_preds, mask_point_targets, avg_factor=num_total_masks * self.num_points) return loss_cls, loss_mask, loss_dice @force_fp32(apply_to=("all_cls_scores", "all_mask_preds")) def loss(self, all_cls_scores, all_mask_preds, gt_labels_list, gt_masks_list, img_metas): """Loss function. Args: all_cls_scores (Tensor): Classification scores for all decoder layers with shape [num_decoder, batch_size, num_queries, cls_out_channels]. all_mask_preds (Tensor): Mask scores for all decoder layers with shape [num_decoder, batch_size, num_queries, h, w]. gt_labels_list (list[Tensor]): Ground truth class indices for each image with shape (n, ). n is the sum of number of stuff type and number of instance in a image. gt_masks_list (list[Tensor]): Ground truth mask for each image with shape (n, h, w). img_metas (list[dict]): List of image meta information. Returns: dict[str, Tensor]: A dictionary of loss components. """ num_dec_layers = len(all_cls_scores) all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)] all_gt_masks_list = [gt_masks_list for _ in range(num_dec_layers)] img_metas_list = [img_metas for _ in range(num_dec_layers)] losses_cls, losses_mask, losses_dice = multi_apply( self.loss_single, all_cls_scores, all_mask_preds, all_gt_labels_list, all_gt_masks_list, img_metas_list ) loss_dict = dict() # loss from the last decoder layer loss_dict["loss_cls"] = losses_cls[-1] loss_dict["loss_mask"] = losses_mask[-1] loss_dict["loss_dice"] = losses_dice[-1] # loss from other decoder layers num_dec_layer = 0 for loss_cls_i, loss_mask_i, loss_dice_i in zip(losses_cls[:-1], losses_mask[:-1], losses_dice[:-1]): loss_dict[f"d{num_dec_layer}.loss_cls"] = loss_cls_i loss_dict[f"d{num_dec_layer}.loss_mask"] = loss_mask_i loss_dict[f"d{num_dec_layer}.loss_dice"] = loss_dice_i num_dec_layer += 1 return loss_dict def forward_head(self, decoder_out, mask_feature, attn_mask_target_size): """Forward for head part which is called after every decoder layer. Args: decoder_out (Tensor): in shape (num_queries, batch_size, c). mask_feature (Tensor): in shape (batch_size, c, h, w). attn_mask_target_size (tuple[int, int]): target attention mask size. Returns: tuple: A tuple contain three elements. - cls_pred (Tensor): Classification scores in shape \ (batch_size, num_queries, cls_out_channels). \ Note `cls_out_channels` should includes background. - mask_pred (Tensor): Mask scores in shape \ (batch_size, num_queries,h, w). - attn_mask (Tensor): Attention mask in shape \ (batch_size * num_heads, num_queries, h, w). """ decoder_out = self.transformer_decoder.post_norm(decoder_out) decoder_out = decoder_out.transpose(0, 1) # shape (num_queries, batch_size, c) cls_pred = self.cls_embed(decoder_out) # shape (num_queries, batch_size, c) mask_embed = self.mask_embed(decoder_out) # shape (num_queries, batch_size, h, w) mask_pred = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_feature) attn_mask = F.interpolate(mask_pred, attn_mask_target_size, mode="bilinear", align_corners=False) # shape (num_queries, batch_size, h, w) -> # (batch_size * num_head, num_queries, h, w) attn_mask = attn_mask.flatten(2).unsqueeze(1).repeat((1, self.num_heads, 1, 1)).flatten(0, 1) attn_mask = attn_mask.sigmoid() < 0.5 attn_mask = attn_mask.detach() return cls_pred, mask_pred, attn_mask def forward(self, feats, img_metas): """Forward function. Args: feats (list[Tensor]): Multi scale Features from the upstream network, each is a 4D-tensor. img_metas (list[dict]): List of image information. Returns: tuple: A tuple contains two elements. - cls_pred_list (list[Tensor)]: Classification logits \ for each decoder layer. Each is a 3D-tensor with shape \ (batch_size, num_queries, cls_out_channels). \ Note `cls_out_channels` should includes background. - mask_pred_list (list[Tensor]): Mask logits for each \ decoder layer. Each with shape (batch_size, num_queries, \ h, w). """ batch_size = len(img_metas) mask_features, multi_scale_memorys = self.pixel_decoder(feats) # multi_scale_memorys (from low resolution to high resolution) decoder_inputs = [] decoder_positional_encodings = [] for i in range(self.num_transformer_feat_level): decoder_input = self.decoder_input_projs[i](multi_scale_memorys[i]) # shape (batch_size, c, h, w) -> (h*w, batch_size, c) decoder_input = decoder_input.flatten(2).permute(2, 0, 1) level_embed = self.level_embed.weight[i].view(1, 1, -1) decoder_input = decoder_input + level_embed # shape (batch_size, c, h, w) -> (h*w, batch_size, c) mask = decoder_input.new_zeros((batch_size,) + multi_scale_memorys[i].shape[-2:], dtype=torch.bool) decoder_positional_encoding = self.decoder_positional_encoding(mask) decoder_positional_encoding = decoder_positional_encoding.flatten(2).permute(2, 0, 1) decoder_inputs.append(decoder_input) decoder_positional_encodings.append(decoder_positional_encoding) # shape (num_queries, c) -> (num_queries, batch_size, c) query_feat = self.query_feat.weight.unsqueeze(1).repeat((1, batch_size, 1)) query_embed = self.query_embed.weight.unsqueeze(1).repeat((1, batch_size, 1)) cls_pred_list = [] mask_pred_list = [] cls_pred, mask_pred, attn_mask = self.forward_head(query_feat, mask_features, multi_scale_memorys[0].shape[-2:]) cls_pred_list.append(cls_pred) mask_pred_list.append(mask_pred) for i in range(self.num_transformer_decoder_layers): level_idx = i % self.num_transformer_feat_level # if a mask is all True(all background), then set it all False. attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False # cross_attn + self_attn layer = self.transformer_decoder.layers[i] attn_masks = [attn_mask, None] query_feat = layer( query=query_feat, key=decoder_inputs[level_idx], value=decoder_inputs[level_idx], query_pos=query_embed, key_pos=decoder_positional_encodings[level_idx], attn_masks=attn_masks, query_key_padding_mask=None, # here we do not apply masking on padded region key_padding_mask=None, ) cls_pred, mask_pred, attn_mask = self.forward_head( query_feat, mask_features, multi_scale_memorys[(i + 1) % self.num_transformer_feat_level].shape[-2:] ) cls_pred_list.append(cls_pred) mask_pred_list.append(mask_pred) return cls_pred_list, mask_pred_list def forward_train(self, x, img_metas, gt_semantic_seg, gt_labels, gt_masks): """Forward function for training mode. Args: x (list[Tensor]): Multi-level features from the upstream network, each is a 4D-tensor. img_metas (list[Dict]): List of image information. gt_semantic_seg (list[tensor]):Each element is the ground truth of semantic segmentation with the shape (N, H, W). train_cfg (dict): The training config, which not been used in maskformer. gt_labels (list[Tensor]): Each element is ground truth labels of each box, shape (num_gts,). gt_masks (list[BitmapMasks]): Each element is masks of instances of a image, shape (num_gts, h, w). Returns: losses (dict[str, Tensor]): a dictionary of loss components """ # forward all_cls_scores, all_mask_preds = self(x, img_metas) # loss losses = self.loss(all_cls_scores, all_mask_preds, gt_labels, gt_masks, img_metas) return losses def forward_test(self, inputs, img_metas, test_cfg): """Test segment without test-time aumengtation. Only the output of last decoder layers was used. Args: inputs (list[Tensor]): Multi-level features from the upstream network, each is a 4D-tensor. img_metas (list[dict]): List of image information. test_cfg (dict): Testing config. Returns: seg_mask (Tensor): Predicted semantic segmentation logits. """ all_cls_scores, all_mask_preds = self(inputs, img_metas) cls_score, mask_pred = all_cls_scores[-1], all_mask_preds[-1] ori_h, ori_w, _ = img_metas[0]["ori_shape"] # semantic inference cls_score = F.softmax(cls_score, dim=-1)[..., :-1] mask_pred = mask_pred.sigmoid() seg_mask = torch.einsum("bqc,bqhw->bchw", cls_score, mask_pred) return seg_mask ================================================ FILE: dinov2/eval/segmentation_m2f/models/losses/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .cross_entropy_loss import CrossEntropyLoss, binary_cross_entropy, cross_entropy, mask_cross_entropy from .dice_loss import DiceLoss from .match_costs import ClassificationCost, CrossEntropyLossCost, DiceCost ================================================ FILE: dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import warnings import torch import torch.nn as nn import torch.nn.functional as F from mmseg.models.builder import LOSSES from mmseg.models.losses.utils import get_class_weight, weight_reduce_loss def cross_entropy( pred, label, weight=None, class_weight=None, reduction="mean", avg_factor=None, ignore_index=-100, avg_non_ignore=False, ): """cross_entropy. The wrapper function for :func:`F.cross_entropy` Args: pred (torch.Tensor): The prediction with shape (N, 1). label (torch.Tensor): The learning label of the prediction. weight (torch.Tensor, optional): Sample-wise loss weight. Default: None. class_weight (list[float], optional): The weight for each class. Default: None. reduction (str, optional): The method used to reduce the loss. Options are 'none', 'mean' and 'sum'. Default: 'mean'. avg_factor (int, optional): Average factor that is used to average the loss. Default: None. ignore_index (int): Specifies a target value that is ignored and does not contribute to the input gradients. When ``avg_non_ignore `` is ``True``, and the ``reduction`` is ``''mean''``, the loss is averaged over non-ignored targets. Defaults: -100. avg_non_ignore (bool): The flag decides to whether the loss is only averaged over non-ignored targets. Default: False. `New in version 0.23.0.` """ # class_weight is a manual rescaling weight given to each class. # If given, has to be a Tensor of size C element-wise losses loss = F.cross_entropy(pred, label, weight=class_weight, reduction="none", ignore_index=ignore_index) # apply weights and do the reduction # average loss over non-ignored elements # pytorch's official cross_entropy average loss over non-ignored elements # refer to https://github.com/pytorch/pytorch/blob/56b43f4fec1f76953f15a627694d4bba34588969/torch/nn/functional.py#L2660 # noqa if (avg_factor is None) and avg_non_ignore and reduction == "mean": avg_factor = label.numel() - (label == ignore_index).sum().item() if weight is not None: weight = weight.float() loss = weight_reduce_loss(loss, weight=weight, reduction=reduction, avg_factor=avg_factor) return loss def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index): """Expand onehot labels to match the size of prediction.""" bin_labels = labels.new_zeros(target_shape) valid_mask = (labels >= 0) & (labels != ignore_index) inds = torch.nonzero(valid_mask, as_tuple=True) if inds[0].numel() > 0: if labels.dim() == 3: bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1 else: bin_labels[inds[0], labels[valid_mask]] = 1 valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float() if label_weights is None: bin_label_weights = valid_mask else: bin_label_weights = label_weights.unsqueeze(1).expand(target_shape) bin_label_weights = bin_label_weights * valid_mask return bin_labels, bin_label_weights, valid_mask def binary_cross_entropy( pred, label, weight=None, reduction="mean", avg_factor=None, class_weight=None, ignore_index=-100, avg_non_ignore=False, **kwargs, ): """Calculate the binary CrossEntropy loss. Args: pred (torch.Tensor): The prediction with shape (N, 1). label (torch.Tensor): The learning label of the prediction. Note: In bce loss, label < 0 is invalid. weight (torch.Tensor, optional): Sample-wise loss weight. reduction (str, optional): The method used to reduce the loss. Options are "none", "mean" and "sum". avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None. class_weight (list[float], optional): The weight for each class. ignore_index (int): The label index to be ignored. Default: -100. avg_non_ignore (bool): The flag decides to whether the loss is only averaged over non-ignored targets. Default: False. `New in version 0.23.0.` Returns: torch.Tensor: The calculated loss """ if pred.size(1) == 1: # For binary class segmentation, the shape of pred is # [N, 1, H, W] and that of label is [N, H, W]. assert label.max() <= 1, "For pred with shape [N, 1, H, W], its label must have at " "most 2 classes" pred = pred.squeeze() if pred.dim() != label.dim(): assert (pred.dim() == 2 and label.dim() == 1) or (pred.dim() == 4 and label.dim() == 3), ( "Only pred shape [N, C], label shape [N] or pred shape [N, C, " "H, W], label shape [N, H, W] are supported" ) # `weight` returned from `_expand_onehot_labels` # has been treated for valid (non-ignore) pixels label, weight, valid_mask = _expand_onehot_labels(label, weight, pred.shape, ignore_index) else: # should mask out the ignored elements valid_mask = ((label >= 0) & (label != ignore_index)).float() if weight is not None: weight = weight * valid_mask else: weight = valid_mask # average loss over non-ignored and valid elements if reduction == "mean" and avg_factor is None and avg_non_ignore: avg_factor = valid_mask.sum().item() loss = F.binary_cross_entropy_with_logits(pred, label.float(), pos_weight=class_weight, reduction="none") # do the reduction for the weighted loss loss = weight_reduce_loss(loss, weight, reduction=reduction, avg_factor=avg_factor) return loss def mask_cross_entropy( pred, target, label, reduction="mean", avg_factor=None, class_weight=None, ignore_index=None, **kwargs ): """Calculate the CrossEntropy loss for masks. Args: pred (torch.Tensor): The prediction with shape (N, C), C is the number of classes. target (torch.Tensor): The learning label of the prediction. label (torch.Tensor): ``label`` indicates the class label of the mask' corresponding object. This will be used to select the mask in the of the class which the object belongs to when the mask prediction if not class-agnostic. reduction (str, optional): The method used to reduce the loss. Options are "none", "mean" and "sum". avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None. class_weight (list[float], optional): The weight for each class. ignore_index (None): Placeholder, to be consistent with other loss. Default: None. Returns: torch.Tensor: The calculated loss """ assert ignore_index is None, "BCE loss does not support ignore_index" assert reduction == "mean" and avg_factor is None num_rois = pred.size()[0] inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device) pred_slice = pred[inds, label].squeeze(1) return F.binary_cross_entropy_with_logits(pred_slice, target, weight=class_weight, reduction="mean")[None] @LOSSES.register_module(force=True) class CrossEntropyLoss(nn.Module): """CrossEntropyLoss. Args: use_sigmoid (bool, optional): Whether the prediction uses sigmoid of softmax. Defaults to False. use_mask (bool, optional): Whether to use mask cross entropy loss. Defaults to False. reduction (str, optional): . Defaults to 'mean'. Options are "none", "mean" and "sum". class_weight (list[float] | str, optional): Weight of each class. If in str format, read them from a file. Defaults to None. loss_weight (float, optional): Weight of the loss. Defaults to 1.0. loss_name (str, optional): Name of the loss item. If you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Defaults to 'loss_ce'. avg_non_ignore (bool): The flag decides to whether the loss is only averaged over non-ignored targets. Default: False. `New in version 0.23.0.` """ def __init__( self, use_sigmoid=False, use_mask=False, reduction="mean", class_weight=None, loss_weight=1.0, loss_name="loss_ce", avg_non_ignore=False, ): super(CrossEntropyLoss, self).__init__() assert (use_sigmoid is False) or (use_mask is False) self.use_sigmoid = use_sigmoid self.use_mask = use_mask self.reduction = reduction self.loss_weight = loss_weight self.class_weight = get_class_weight(class_weight) self.avg_non_ignore = avg_non_ignore if not self.avg_non_ignore and self.reduction == "mean": warnings.warn( "Default ``avg_non_ignore`` is False, if you would like to " "ignore the certain label and average loss over non-ignore " "labels, which is the same with PyTorch official " "cross_entropy, set ``avg_non_ignore=True``." ) if self.use_sigmoid: self.cls_criterion = binary_cross_entropy elif self.use_mask: self.cls_criterion = mask_cross_entropy else: self.cls_criterion = cross_entropy self._loss_name = loss_name def extra_repr(self): """Extra repr.""" s = f"avg_non_ignore={self.avg_non_ignore}" return s def forward( self, cls_score, label, weight=None, avg_factor=None, reduction_override=None, ignore_index=-100, **kwargs ): """Forward function.""" assert reduction_override in (None, "none", "mean", "sum") reduction = reduction_override if reduction_override else self.reduction if self.class_weight is not None: class_weight = cls_score.new_tensor(self.class_weight) else: class_weight = None # Note: for BCE loss, label < 0 is invalid. loss_cls = self.loss_weight * self.cls_criterion( cls_score, label, weight, class_weight=class_weight, reduction=reduction, avg_factor=avg_factor, avg_non_ignore=self.avg_non_ignore, ignore_index=ignore_index, **kwargs, ) return loss_cls @property def loss_name(self): """Loss Name. This function must be implemented and will return the name of this loss function. This name will be used to combine different loss items by simple sum operation. In addition, if you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Returns: str: The name of this loss item. """ return self._loss_name ================================================ FILE: dinov2/eval/segmentation_m2f/models/losses/dice_loss.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn from mmseg.models.builder import LOSSES from mmseg.models.losses.utils import weight_reduce_loss def dice_loss(pred, target, weight=None, eps=1e-3, reduction="mean", avg_factor=None): """Calculate dice loss, which is proposed in `V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation `_. Args: pred (torch.Tensor): The prediction, has a shape (n, *) target (torch.Tensor): The learning label of the prediction, shape (n, *), same shape of pred. weight (torch.Tensor, optional): The weight of loss for each prediction, has a shape (n,). Defaults to None. eps (float): Avoid dividing by zero. Default: 1e-3. reduction (str, optional): The method used to reduce the loss into a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum". avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None. """ input = pred.flatten(1) target = target.flatten(1).float() a = torch.sum(input * target, 1) b = torch.sum(input * input, 1) + eps c = torch.sum(target * target, 1) + eps d = (2 * a) / (b + c) loss = 1 - d if weight is not None: assert weight.ndim == loss.ndim assert len(weight) == len(pred) loss = weight_reduce_loss(loss, weight, reduction, avg_factor) return loss def naive_dice_loss(pred, target, weight=None, eps=1e-3, reduction="mean", avg_factor=None): """Calculate naive dice loss, the coefficient in the denominator is the first power instead of the second power. Args: pred (torch.Tensor): The prediction, has a shape (n, *) target (torch.Tensor): The learning label of the prediction, shape (n, *), same shape of pred. weight (torch.Tensor, optional): The weight of loss for each prediction, has a shape (n,). Defaults to None. eps (float): Avoid dividing by zero. Default: 1e-3. reduction (str, optional): The method used to reduce the loss into a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum". avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None. """ input = pred.flatten(1) target = target.flatten(1).float() a = torch.sum(input * target, 1) b = torch.sum(input, 1) c = torch.sum(target, 1) d = (2 * a + eps) / (b + c + eps) loss = 1 - d if weight is not None: assert weight.ndim == loss.ndim assert len(weight) == len(pred) loss = weight_reduce_loss(loss, weight, reduction, avg_factor) return loss @LOSSES.register_module(force=True) class DiceLoss(nn.Module): def __init__(self, use_sigmoid=True, activate=True, reduction="mean", naive_dice=False, loss_weight=1.0, eps=1e-3): """Dice Loss, there are two forms of dice loss is supported: - the one proposed in `V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation `_. - the dice loss in which the power of the number in the denominator is the first power instead of the second power. Args: use_sigmoid (bool, optional): Whether to the prediction is used for sigmoid or softmax. Defaults to True. activate (bool): Whether to activate the predictions inside, this will disable the inside sigmoid operation. Defaults to True. reduction (str, optional): The method used to reduce the loss. Options are "none", "mean" and "sum". Defaults to 'mean'. naive_dice (bool, optional): If false, use the dice loss defined in the V-Net paper, otherwise, use the naive dice loss in which the power of the number in the denominator is the first power instead of the second power.Defaults to False. loss_weight (float, optional): Weight of loss. Defaults to 1.0. eps (float): Avoid dividing by zero. Defaults to 1e-3. """ super(DiceLoss, self).__init__() self.use_sigmoid = use_sigmoid self.reduction = reduction self.naive_dice = naive_dice self.loss_weight = loss_weight self.eps = eps self.activate = activate def forward(self, pred, target, weight=None, reduction_override=None, avg_factor=None): """Forward function. Args: pred (torch.Tensor): The prediction, has a shape (n, *). target (torch.Tensor): The label of the prediction, shape (n, *), same shape of pred. weight (torch.Tensor, optional): The weight of loss for each prediction, has a shape (n,). Defaults to None. avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None. reduction_override (str, optional): The reduction method used to override the original reduction method of the loss. Options are "none", "mean" and "sum". Returns: torch.Tensor: The calculated loss """ assert reduction_override in (None, "none", "mean", "sum") reduction = reduction_override if reduction_override else self.reduction if self.activate: if self.use_sigmoid: pred = pred.sigmoid() else: raise NotImplementedError if self.naive_dice: loss = self.loss_weight * naive_dice_loss( pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor ) else: loss = self.loss_weight * dice_loss( pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor ) return loss ================================================ FILE: dinov2/eval/segmentation_m2f/models/losses/match_costs.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn.functional as F from ..builder import MATCH_COST @MATCH_COST.register_module() class ClassificationCost: """ClsSoftmaxCost.Borrow from mmdet.core.bbox.match_costs.match_cost.ClassificationCost. Args: weight (int | float, optional): loss_weight Examples: >>> import torch >>> self = ClassificationCost() >>> cls_pred = torch.rand(4, 3) >>> gt_labels = torch.tensor([0, 1, 2]) >>> factor = torch.tensor([10, 8, 10, 8]) >>> self(cls_pred, gt_labels) tensor([[-0.3430, -0.3525, -0.3045], [-0.3077, -0.2931, -0.3992], [-0.3664, -0.3455, -0.2881], [-0.3343, -0.2701, -0.3956]]) """ def __init__(self, weight=1.0): self.weight = weight def __call__(self, cls_pred, gt_labels): """ Args: cls_pred (Tensor): Predicted classification logits, shape [num_query, num_class]. gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,). Returns: torch.Tensor: cls_cost value with weight """ # Following the official DETR repo, contrary to the loss that # NLL is used, we approximate it in 1 - cls_score[gt_label]. # The 1 is a constant that doesn't change the matching, # so it can be omitted. cls_score = cls_pred.softmax(-1) cls_cost = -cls_score[:, gt_labels] return cls_cost * self.weight @MATCH_COST.register_module() class DiceCost: """Cost of mask assignments based on dice losses. Args: weight (int | float, optional): loss_weight. Defaults to 1. pred_act (bool, optional): Whether to apply sigmoid to mask_pred. Defaults to False. eps (float, optional): default 1e-12. """ def __init__(self, weight=1.0, pred_act=False, eps=1e-3): self.weight = weight self.pred_act = pred_act self.eps = eps def binary_mask_dice_loss(self, mask_preds, gt_masks): """ Args: mask_preds (Tensor): Mask prediction in shape (N1, H, W). gt_masks (Tensor): Ground truth in shape (N2, H, W) store 0 or 1, 0 for negative class and 1 for positive class. Returns: Tensor: Dice cost matrix in shape (N1, N2). """ mask_preds = mask_preds.reshape((mask_preds.shape[0], -1)) gt_masks = gt_masks.reshape((gt_masks.shape[0], -1)).float() numerator = 2 * torch.einsum("nc,mc->nm", mask_preds, gt_masks) denominator = mask_preds.sum(-1)[:, None] + gt_masks.sum(-1)[None, :] loss = 1 - (numerator + self.eps) / (denominator + self.eps) return loss def __call__(self, mask_preds, gt_masks): """ Args: mask_preds (Tensor): Mask prediction logits in shape (N1, H, W). gt_masks (Tensor): Ground truth in shape (N2, H, W). Returns: Tensor: Dice cost matrix in shape (N1, N2). """ if self.pred_act: mask_preds = mask_preds.sigmoid() dice_cost = self.binary_mask_dice_loss(mask_preds, gt_masks) return dice_cost * self.weight @MATCH_COST.register_module() class CrossEntropyLossCost: """CrossEntropyLossCost. Args: weight (int | float, optional): loss weight. Defaults to 1. use_sigmoid (bool, optional): Whether the prediction uses sigmoid of softmax. Defaults to True. """ def __init__(self, weight=1.0, use_sigmoid=True): assert use_sigmoid, "use_sigmoid = False is not supported yet." self.weight = weight self.use_sigmoid = use_sigmoid def _binary_cross_entropy(self, cls_pred, gt_labels): """ Args: cls_pred (Tensor): The prediction with shape (num_query, 1, *) or (num_query, *). gt_labels (Tensor): The learning label of prediction with shape (num_gt, *). Returns: Tensor: Cross entropy cost matrix in shape (num_query, num_gt). """ cls_pred = cls_pred.flatten(1).float() gt_labels = gt_labels.flatten(1).float() n = cls_pred.shape[1] pos = F.binary_cross_entropy_with_logits(cls_pred, torch.ones_like(cls_pred), reduction="none") neg = F.binary_cross_entropy_with_logits(cls_pred, torch.zeros_like(cls_pred), reduction="none") cls_cost = torch.einsum("nc,mc->nm", pos, gt_labels) + torch.einsum("nc,mc->nm", neg, 1 - gt_labels) cls_cost = cls_cost / n return cls_cost def __call__(self, cls_pred, gt_labels): """ Args: cls_pred (Tensor): Predicted classification logits. gt_labels (Tensor): Labels. Returns: Tensor: Cross entropy cost matrix with weight in shape (num_query, num_gt). """ if self.use_sigmoid: cls_cost = self._binary_cross_entropy(cls_pred, gt_labels) else: raise NotImplementedError return cls_cost * self.weight ================================================ FILE: dinov2/eval/segmentation_m2f/models/plugins/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .msdeformattn_pixel_decoder import MSDeformAttnPixelDecoder ================================================ FILE: dinov2/eval/segmentation_m2f/models/plugins/msdeformattn_pixel_decoder.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import PLUGIN_LAYERS, Conv2d, ConvModule, caffe2_xavier_init, normal_init, xavier_init from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer_sequence from mmcv.runner import BaseModule, ModuleList from ...core.anchor import MlvlPointGenerator from ..utils.transformer import MultiScaleDeformableAttention @PLUGIN_LAYERS.register_module() class MSDeformAttnPixelDecoder(BaseModule): """Pixel decoder with multi-scale deformable attention. Args: in_channels (list[int] | tuple[int]): Number of channels in the input feature maps. strides (list[int] | tuple[int]): Output strides of feature from backbone. feat_channels (int): Number of channels for feature. out_channels (int): Number of channels for output. num_outs (int): Number of output scales. norm_cfg (:obj:`mmcv.ConfigDict` | dict): Config for normalization. Defaults to dict(type='GN', num_groups=32). act_cfg (:obj:`mmcv.ConfigDict` | dict): Config for activation. Defaults to dict(type='ReLU'). encoder (:obj:`mmcv.ConfigDict` | dict): Config for transformer encoder. Defaults to `DetrTransformerEncoder`. positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for transformer encoder position encoding. Defaults to dict(type='SinePositionalEncoding', num_feats=128, normalize=True). init_cfg (:obj:`mmcv.ConfigDict` | dict): Initialization config dict. """ def __init__( self, in_channels=[256, 512, 1024, 2048], strides=[4, 8, 16, 32], feat_channels=256, out_channels=256, num_outs=3, norm_cfg=dict(type="GN", num_groups=32), act_cfg=dict(type="ReLU"), encoder=dict( type="DetrTransformerEncoder", num_layers=6, transformerlayers=dict( type="BaseTransformerLayer", attn_cfgs=dict( type="MultiScaleDeformableAttention", embed_dims=256, num_heads=8, num_levels=3, num_points=4, im2col_step=64, dropout=0.0, batch_first=False, norm_cfg=None, init_cfg=None, ), feedforward_channels=1024, ffn_dropout=0.0, operation_order=("self_attn", "norm", "ffn", "norm"), ), init_cfg=None, ), positional_encoding=dict(type="SinePositionalEncoding", num_feats=128, normalize=True), init_cfg=None, ): super().__init__(init_cfg=init_cfg) self.strides = strides self.num_input_levels = len(in_channels) self.num_encoder_levels = encoder.transformerlayers.attn_cfgs.num_levels assert self.num_encoder_levels >= 1, "num_levels in attn_cfgs must be at least one" input_conv_list = [] # from top to down (low to high resolution) for i in range(self.num_input_levels - 1, self.num_input_levels - self.num_encoder_levels - 1, -1): input_conv = ConvModule( in_channels[i], feat_channels, kernel_size=1, norm_cfg=norm_cfg, act_cfg=None, bias=True ) input_conv_list.append(input_conv) self.input_convs = ModuleList(input_conv_list) self.encoder = build_transformer_layer_sequence(encoder) self.postional_encoding = build_positional_encoding(positional_encoding) # high resolution to low resolution self.level_encoding = nn.Embedding(self.num_encoder_levels, feat_channels) # fpn-like structure self.lateral_convs = ModuleList() self.output_convs = ModuleList() self.use_bias = norm_cfg is None # from top to down (low to high resolution) # fpn for the rest features that didn't pass in encoder for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1, -1): lateral_conv = ConvModule( in_channels[i], feat_channels, kernel_size=1, bias=self.use_bias, norm_cfg=norm_cfg, act_cfg=None ) output_conv = ConvModule( feat_channels, feat_channels, kernel_size=3, stride=1, padding=1, bias=self.use_bias, norm_cfg=norm_cfg, act_cfg=act_cfg, ) self.lateral_convs.append(lateral_conv) self.output_convs.append(output_conv) self.mask_feature = Conv2d(feat_channels, out_channels, kernel_size=1, stride=1, padding=0) self.num_outs = num_outs self.point_generator = MlvlPointGenerator(strides) def init_weights(self): """Initialize weights.""" for i in range(0, self.num_encoder_levels): xavier_init(self.input_convs[i].conv, gain=1, bias=0, distribution="uniform") for i in range(0, self.num_input_levels - self.num_encoder_levels): caffe2_xavier_init(self.lateral_convs[i].conv, bias=0) caffe2_xavier_init(self.output_convs[i].conv, bias=0) caffe2_xavier_init(self.mask_feature, bias=0) normal_init(self.level_encoding, mean=0, std=1) for p in self.encoder.parameters(): if p.dim() > 1: nn.init.xavier_normal_(p) # init_weights defined in MultiScaleDeformableAttention for layer in self.encoder.layers: for attn in layer.attentions: if isinstance(attn, MultiScaleDeformableAttention): attn.init_weights() def forward(self, feats): """ Args: feats (list[Tensor]): Feature maps of each level. Each has shape of (batch_size, c, h, w). Returns: tuple: A tuple containing the following: - mask_feature (Tensor): shape (batch_size, c, h, w). - multi_scale_features (list[Tensor]): Multi scale \ features, each in shape (batch_size, c, h, w). """ # generate padding mask for each level, for each image batch_size = feats[0].shape[0] encoder_input_list = [] padding_mask_list = [] level_positional_encoding_list = [] spatial_shapes = [] reference_points_list = [] for i in range(self.num_encoder_levels): level_idx = self.num_input_levels - i - 1 feat = feats[level_idx] feat_projected = self.input_convs[i](feat) h, w = feat.shape[-2:] # no padding padding_mask_resized = feat.new_zeros((batch_size,) + feat.shape[-2:], dtype=torch.bool) pos_embed = self.postional_encoding(padding_mask_resized) level_embed = self.level_encoding.weight[i] level_pos_embed = level_embed.view(1, -1, 1, 1) + pos_embed # (h_i * w_i, 2) reference_points = self.point_generator.single_level_grid_priors( feat.shape[-2:], level_idx, device=feat.device ) # normalize factor = feat.new_tensor([[w, h]]) * self.strides[level_idx] reference_points = reference_points / factor # shape (batch_size, c, h_i, w_i) -> (h_i * w_i, batch_size, c) feat_projected = feat_projected.flatten(2).permute(2, 0, 1) level_pos_embed = level_pos_embed.flatten(2).permute(2, 0, 1) padding_mask_resized = padding_mask_resized.flatten(1) encoder_input_list.append(feat_projected) padding_mask_list.append(padding_mask_resized) level_positional_encoding_list.append(level_pos_embed) spatial_shapes.append(feat.shape[-2:]) reference_points_list.append(reference_points) # shape (batch_size, total_num_query), # total_num_query=sum([., h_i * w_i,.]) padding_masks = torch.cat(padding_mask_list, dim=1) # shape (total_num_query, batch_size, c) encoder_inputs = torch.cat(encoder_input_list, dim=0) level_positional_encodings = torch.cat(level_positional_encoding_list, dim=0) device = encoder_inputs.device # shape (num_encoder_levels, 2), from low # resolution to high resolution spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=device) # shape (0, h_0*w_0, h_0*w_0+h_1*w_1, ...) level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) reference_points = torch.cat(reference_points_list, dim=0) reference_points = reference_points[None, :, None].repeat(batch_size, 1, self.num_encoder_levels, 1) valid_radios = reference_points.new_ones((batch_size, self.num_encoder_levels, 2)) # shape (num_total_query, batch_size, c) memory = self.encoder( query=encoder_inputs, key=None, value=None, query_pos=level_positional_encodings, key_pos=None, attn_masks=None, key_padding_mask=None, query_key_padding_mask=padding_masks, spatial_shapes=spatial_shapes, reference_points=reference_points, level_start_index=level_start_index, valid_radios=valid_radios, ) # (num_total_query, batch_size, c) -> (batch_size, c, num_total_query) memory = memory.permute(1, 2, 0) # from low resolution to high resolution num_query_per_level = [e[0] * e[1] for e in spatial_shapes] outs = torch.split(memory, num_query_per_level, dim=-1) outs = [x.reshape(batch_size, -1, spatial_shapes[i][0], spatial_shapes[i][1]) for i, x in enumerate(outs)] for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1, -1): x = feats[i] cur_feat = self.lateral_convs[i](x) y = cur_feat + F.interpolate(outs[-1], size=cur_feat.shape[-2:], mode="bilinear", align_corners=False) y = self.output_convs[i](y) outs.append(y) multi_scale_features = outs[: self.num_outs] mask_feature = self.mask_feature(outs[-1]) return mask_feature, multi_scale_features ================================================ FILE: dinov2/eval/segmentation_m2f/models/segmentors/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .encoder_decoder_mask2former import EncoderDecoderMask2Former ================================================ FILE: dinov2/eval/segmentation_m2f/models/segmentors/encoder_decoder_mask2former.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn import torch.nn.functional as F from mmseg.core import add_prefix from mmseg.models import builder from mmseg.models.builder import SEGMENTORS from mmseg.models.segmentors.base import BaseSegmentor from mmseg.ops import resize @SEGMENTORS.register_module() class EncoderDecoderMask2Former(BaseSegmentor): """Encoder Decoder segmentors. EncoderDecoder typically consists of backbone, decode_head, auxiliary_head. Note that auxiliary_head is only used for deep supervision during training, which could be dumped during inference. """ def __init__( self, backbone, decode_head, neck=None, auxiliary_head=None, train_cfg=None, test_cfg=None, pretrained=None, init_cfg=None, ): super(EncoderDecoderMask2Former, self).__init__(init_cfg) if pretrained is not None: assert backbone.get("pretrained") is None, "both backbone and segmentor set pretrained weight" backbone.pretrained = pretrained self.backbone = builder.build_backbone(backbone) if neck is not None: self.neck = builder.build_neck(neck) decode_head.update(train_cfg=train_cfg) decode_head.update(test_cfg=test_cfg) self._init_decode_head(decode_head) self._init_auxiliary_head(auxiliary_head) self.train_cfg = train_cfg self.test_cfg = test_cfg assert self.with_decode_head def _init_decode_head(self, decode_head): """Initialize ``decode_head``""" self.decode_head = builder.build_head(decode_head) self.align_corners = self.decode_head.align_corners self.num_classes = self.decode_head.num_classes def _init_auxiliary_head(self, auxiliary_head): """Initialize ``auxiliary_head``""" if auxiliary_head is not None: if isinstance(auxiliary_head, list): self.auxiliary_head = nn.ModuleList() for head_cfg in auxiliary_head: self.auxiliary_head.append(builder.build_head(head_cfg)) else: self.auxiliary_head = builder.build_head(auxiliary_head) def extract_feat(self, img): """Extract features from images.""" x = self.backbone(img) if self.with_neck: x = self.neck(x) return x def encode_decode(self, img, img_metas): """Encode images with backbone and decode into a semantic segmentation map of the same size as input.""" x = self.extract_feat(img) out = self._decode_head_forward_test(x, img_metas) out = resize(input=out, size=img.shape[2:], mode="bilinear", align_corners=self.align_corners) return out def _decode_head_forward_train(self, x, img_metas, gt_semantic_seg, **kwargs): """Run forward function and calculate loss for decode head in training.""" losses = dict() loss_decode = self.decode_head.forward_train(x, img_metas, gt_semantic_seg, **kwargs) losses.update(add_prefix(loss_decode, "decode")) return losses def _decode_head_forward_test(self, x, img_metas): """Run forward function and calculate loss for decode head in inference.""" seg_logits = self.decode_head.forward_test(x, img_metas, self.test_cfg) return seg_logits def _auxiliary_head_forward_train(self, x, img_metas, gt_semantic_seg): """Run forward function and calculate loss for auxiliary head in training.""" losses = dict() if isinstance(self.auxiliary_head, nn.ModuleList): for idx, aux_head in enumerate(self.auxiliary_head): loss_aux = aux_head.forward_train(x, img_metas, gt_semantic_seg, self.train_cfg) losses.update(add_prefix(loss_aux, f"aux_{idx}")) else: loss_aux = self.auxiliary_head.forward_train(x, img_metas, gt_semantic_seg, self.train_cfg) losses.update(add_prefix(loss_aux, "aux")) return losses def forward_dummy(self, img): """Dummy forward function.""" seg_logit = self.encode_decode(img, None) return seg_logit def forward_train(self, img, img_metas, gt_semantic_seg, **kwargs): """Forward function for training. Args: img (Tensor): Input images. img_metas (list[dict]): List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `mmseg/datasets/pipelines/formatting.py:Collect`. gt_semantic_seg (Tensor): Semantic segmentation masks used if the architecture supports semantic segmentation task. Returns: dict[str, Tensor]: a dictionary of loss components """ x = self.extract_feat(img) losses = dict() loss_decode = self._decode_head_forward_train(x, img_metas, gt_semantic_seg, **kwargs) losses.update(loss_decode) if self.with_auxiliary_head: loss_aux = self._auxiliary_head_forward_train(x, img_metas, gt_semantic_seg) losses.update(loss_aux) return losses def slide_inference(self, img, img_meta, rescale): """Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding. """ h_stride, w_stride = self.test_cfg.stride h_crop, w_crop = self.test_cfg.crop_size batch_size, _, h_img, w_img = img.size() num_classes = self.num_classes h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1 w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1 preds = img.new_zeros((batch_size, num_classes, h_img, w_img)) count_mat = img.new_zeros((batch_size, 1, h_img, w_img)) for h_idx in range(h_grids): for w_idx in range(w_grids): y1 = h_idx * h_stride x1 = w_idx * w_stride y2 = min(y1 + h_crop, h_img) x2 = min(x1 + w_crop, w_img) y1 = max(y2 - h_crop, 0) x1 = max(x2 - w_crop, 0) crop_img = img[:, :, y1:y2, x1:x2] crop_seg_logit = self.encode_decode(crop_img, img_meta) preds += F.pad(crop_seg_logit, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2))) count_mat[:, :, y1:y2, x1:x2] += 1 assert (count_mat == 0).sum() == 0 if torch.onnx.is_in_onnx_export(): # cast count_mat to constant while exporting to ONNX count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device) preds = preds / count_mat if rescale: preds = resize( preds, size=img_meta[0]["ori_shape"][:2], mode="bilinear", align_corners=self.align_corners, warning=False, ) return preds def whole_inference(self, img, img_meta, rescale): """Inference with full image.""" seg_logit = self.encode_decode(img, img_meta) if rescale: # support dynamic shape for onnx if torch.onnx.is_in_onnx_export(): size = img.shape[2:] else: size = img_meta[0]["ori_shape"][:2] seg_logit = resize(seg_logit, size=size, mode="bilinear", align_corners=self.align_corners, warning=False) return seg_logit def inference(self, img, img_meta, rescale): """Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `mmseg/datasets/pipelines/formatting.py:Collect`. rescale (bool): Whether rescale back to original shape. Returns: Tensor: The output segmentation map. """ assert self.test_cfg.mode in ["slide", "whole"] ori_shape = img_meta[0]["ori_shape"] assert all(_["ori_shape"] == ori_shape for _ in img_meta) if self.test_cfg.mode == "slide": seg_logit = self.slide_inference(img, img_meta, rescale) else: seg_logit = self.whole_inference(img, img_meta, rescale) output = F.softmax(seg_logit, dim=1) flip = img_meta[0]["flip"] if flip: flip_direction = img_meta[0]["flip_direction"] assert flip_direction in ["horizontal", "vertical"] if flip_direction == "horizontal": output = output.flip(dims=(3,)) elif flip_direction == "vertical": output = output.flip(dims=(2,)) return output def simple_test(self, img, img_meta, rescale=True): """Simple test with single image.""" seg_logit = self.inference(img, img_meta, rescale) seg_pred = seg_logit.argmax(dim=1) if torch.onnx.is_in_onnx_export(): # our inference backend only support 4D output seg_pred = seg_pred.unsqueeze(0) return seg_pred seg_pred = seg_pred.cpu().numpy() # unravel batch dim seg_pred = list(seg_pred) return seg_pred def aug_test(self, imgs, img_metas, rescale=True): """Test with augmentations. Only rescale=True is supported. """ # aug_test rescale all imgs back to ori_shape for now assert rescale # to save memory, we get augmented seg logit inplace seg_logit = self.inference(imgs[0], img_metas[0], rescale) for i in range(1, len(imgs)): cur_seg_logit = self.inference(imgs[i], img_metas[i], rescale) seg_logit += cur_seg_logit seg_logit /= len(imgs) seg_pred = seg_logit.argmax(dim=1) seg_pred = seg_pred.cpu().numpy() # unravel batch dim seg_pred = list(seg_pred) return seg_pred ================================================ FILE: dinov2/eval/segmentation_m2f/models/utils/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .assigner import MaskHungarianAssigner from .point_sample import get_uncertain_point_coords_with_randomness from .positional_encoding import LearnedPositionalEncoding, SinePositionalEncoding from .transformer import DetrTransformerDecoder, DetrTransformerDecoderLayer, DynamicConv, Transformer ================================================ FILE: dinov2/eval/segmentation_m2f/models/utils/assigner.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from abc import ABCMeta, abstractmethod import torch from ..builder import MASK_ASSIGNERS, build_match_cost try: from scipy.optimize import linear_sum_assignment except ImportError: linear_sum_assignment = None class AssignResult(metaclass=ABCMeta): """Collection of assign results.""" def __init__(self, num_gts, gt_inds, labels): self.num_gts = num_gts self.gt_inds = gt_inds self.labels = labels @property def info(self): info = { "num_gts": self.num_gts, "gt_inds": self.gt_inds, "labels": self.labels, } return info class BaseAssigner(metaclass=ABCMeta): """Base assigner that assigns boxes to ground truth boxes.""" @abstractmethod def assign(self, masks, gt_masks, gt_masks_ignore=None, gt_labels=None): """Assign boxes to either a ground truth boxes or a negative boxes.""" pass @MASK_ASSIGNERS.register_module() class MaskHungarianAssigner(BaseAssigner): """Computes one-to-one matching between predictions and ground truth for mask. This class computes an assignment between the targets and the predictions based on the costs. The costs are weighted sum of three components: classification cost, regression L1 cost and regression iou cost. The targets don't include the no_object, so generally there are more predictions than targets. After the one-to-one matching, the un-matched are treated as backgrounds. Thus each query prediction will be assigned with `0` or a positive integer indicating the ground truth index: - 0: negative sample, no assigned gt - positive integer: positive sample, index (1-based) of assigned gt Args: cls_cost (obj:`mmcv.ConfigDict`|dict): Classification cost config. mask_cost (obj:`mmcv.ConfigDict`|dict): Mask cost config. dice_cost (obj:`mmcv.ConfigDict`|dict): Dice cost config. """ def __init__( self, cls_cost=dict(type="ClassificationCost", weight=1.0), dice_cost=dict(type="DiceCost", weight=1.0), mask_cost=dict(type="MaskFocalCost", weight=1.0), ): self.cls_cost = build_match_cost(cls_cost) self.dice_cost = build_match_cost(dice_cost) self.mask_cost = build_match_cost(mask_cost) def assign(self, cls_pred, mask_pred, gt_labels, gt_masks, img_meta, gt_masks_ignore=None, eps=1e-7): """Computes one-to-one matching based on the weighted costs. This method assign each query prediction to a ground truth or background. The `assigned_gt_inds` with -1 means don't care, 0 means negative sample, and positive number is the index (1-based) of assigned gt. The assignment is done in the following steps, the order matters. 1. assign every prediction to -1 2. compute the weighted costs 3. do Hungarian matching on CPU based on the costs 4. assign all to 0 (background) first, then for each matched pair between predictions and gts, treat this prediction as foreground and assign the corresponding gt index (plus 1) to it. Args: mask_pred (Tensor): Predicted mask, shape [num_query, h, w] cls_pred (Tensor): Predicted classification logits, shape [num_query, num_class]. gt_masks (Tensor): Ground truth mask, shape [num_gt, h, w]. gt_labels (Tensor): Label of `gt_masks`, shape (num_gt,). img_meta (dict): Meta information for current image. gt_masks_ignore (Tensor, optional): Ground truth masks that are labelled as `ignored`. Default None. eps (int | float, optional): A value added to the denominator for numerical stability. Default 1e-7. Returns: :obj:`AssignResult`: The assigned result. """ assert gt_masks_ignore is None, "Only case when gt_masks_ignore is None is supported." num_gts, num_queries = gt_labels.shape[0], cls_pred.shape[0] # 1. assign -1 by default assigned_gt_inds = cls_pred.new_full((num_queries,), -1, dtype=torch.long) assigned_labels = cls_pred.new_full((num_queries,), -1, dtype=torch.long) if num_gts == 0 or num_queries == 0: # No ground truth or boxes, return empty assignment if num_gts == 0: # No ground truth, assign all to background assigned_gt_inds[:] = 0 return AssignResult(num_gts, assigned_gt_inds, labels=assigned_labels) # 2. compute the weighted costs # classification and maskcost. if self.cls_cost.weight != 0 and cls_pred is not None: cls_cost = self.cls_cost(cls_pred, gt_labels) else: cls_cost = 0 if self.mask_cost.weight != 0: # mask_pred shape = [nq, h, w] # gt_mask shape = [ng, h, w] # mask_cost shape = [nq, ng] mask_cost = self.mask_cost(mask_pred, gt_masks) else: mask_cost = 0 if self.dice_cost.weight != 0: dice_cost = self.dice_cost(mask_pred, gt_masks) else: dice_cost = 0 cost = cls_cost + mask_cost + dice_cost # 3. do Hungarian matching on CPU using linear_sum_assignment cost = cost.detach().cpu() if linear_sum_assignment is None: raise ImportError('Please run "pip install scipy" ' "to install scipy first.") matched_row_inds, matched_col_inds = linear_sum_assignment(cost) matched_row_inds = torch.from_numpy(matched_row_inds).to(cls_pred.device) matched_col_inds = torch.from_numpy(matched_col_inds).to(cls_pred.device) # 4. assign backgrounds and foregrounds # assign all indices to backgrounds first assigned_gt_inds[:] = 0 # assign foregrounds based on matching results assigned_gt_inds[matched_row_inds] = matched_col_inds + 1 assigned_labels[matched_row_inds] = gt_labels[matched_col_inds] return AssignResult(num_gts, assigned_gt_inds, labels=assigned_labels) ================================================ FILE: dinov2/eval/segmentation_m2f/models/utils/point_sample.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch from mmcv.ops import point_sample def get_uncertainty(mask_pred, labels): """Estimate uncertainty based on pred logits. We estimate uncertainty as L1 distance between 0.0 and the logits prediction in 'mask_pred' for the foreground class in `classes`. Args: mask_pred (Tensor): mask predication logits, shape (num_rois, num_classes, mask_height, mask_width). labels (list[Tensor]): Either predicted or ground truth label for each predicted mask, of length num_rois. Returns: scores (Tensor): Uncertainty scores with the most uncertain locations having the highest uncertainty score, shape (num_rois, 1, mask_height, mask_width) """ if mask_pred.shape[1] == 1: gt_class_logits = mask_pred.clone() else: inds = torch.arange(mask_pred.shape[0], device=mask_pred.device) gt_class_logits = mask_pred[inds, labels].unsqueeze(1) return -torch.abs(gt_class_logits) def get_uncertain_point_coords_with_randomness( mask_pred, labels, num_points, oversample_ratio, importance_sample_ratio ): """Get ``num_points`` most uncertain points with random points during train. Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The uncertainties are calculated for each point using 'get_uncertainty()' function that takes point's logit prediction as input. Args: mask_pred (Tensor): A tensor of shape (num_rois, num_classes, mask_height, mask_width) for class-specific or class-agnostic prediction. labels (list): The ground truth class for each instance. num_points (int): The number of points to sample. oversample_ratio (int): Oversampling parameter. importance_sample_ratio (float): Ratio of points that are sampled via importnace sampling. Returns: point_coords (Tensor): A tensor of shape (num_rois, num_points, 2) that contains the coordinates sampled points. """ assert oversample_ratio >= 1 assert 0 <= importance_sample_ratio <= 1 batch_size = mask_pred.shape[0] num_sampled = int(num_points * oversample_ratio) point_coords = torch.rand(batch_size, num_sampled, 2, device=mask_pred.device) point_logits = point_sample(mask_pred, point_coords) # It is crucial to calculate uncertainty based on the sampled # prediction value for the points. Calculating uncertainties of the # coarse predictions first and sampling them for points leads to # incorrect results. To illustrate this: assume uncertainty func( # logits)=-abs(logits), a sampled point between two coarse # predictions with -1 and 1 logits has 0 logits, and therefore 0 # uncertainty value. However, if we calculate uncertainties for the # coarse predictions first, both will have -1 uncertainty, # and sampled point will get -1 uncertainty. point_uncertainties = get_uncertainty(point_logits, labels) num_uncertain_points = int(importance_sample_ratio * num_points) num_random_points = num_points - num_uncertain_points idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1] shift = num_sampled * torch.arange(batch_size, dtype=torch.long, device=mask_pred.device) idx += shift[:, None] point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(batch_size, num_uncertain_points, 2) if num_random_points > 0: rand_roi_coords = torch.rand(batch_size, num_random_points, 2, device=mask_pred.device) point_coords = torch.cat((point_coords, rand_roi_coords), dim=1) return point_coords ================================================ FILE: dinov2/eval/segmentation_m2f/models/utils/positional_encoding.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import math import torch import torch.nn as nn from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING from mmcv.runner import BaseModule @POSITIONAL_ENCODING.register_module() class SinePositionalEncoding(BaseModule): """Position encoding with sine and cosine functions. See `End-to-End Object Detection with Transformers `_ for details. Args: num_feats (int): The feature dimension for each position along x-axis or y-axis. Note the final returned dimension for each position is 2 times of this value. temperature (int, optional): The temperature used for scaling the position embedding. Defaults to 10000. normalize (bool, optional): Whether to normalize the position embedding. Defaults to False. scale (float, optional): A scale factor that scales the position embedding. The scale will be used only when `normalize` is True. Defaults to 2*pi. eps (float, optional): A value added to the denominator for numerical stability. Defaults to 1e-6. offset (float): offset add to embed when do the normalization. Defaults to 0. init_cfg (dict or list[dict], optional): Initialization config dict. Default: None """ def __init__( self, num_feats, temperature=10000, normalize=False, scale=2 * math.pi, eps=1e-6, offset=0.0, init_cfg=None ): super(SinePositionalEncoding, self).__init__(init_cfg) if normalize: assert isinstance(scale, (float, int)), ( "when normalize is set," "scale should be provided and in float or int type, " f"found {type(scale)}" ) self.num_feats = num_feats self.temperature = temperature self.normalize = normalize self.scale = scale self.eps = eps self.offset = offset def forward(self, mask): """Forward function for `SinePositionalEncoding`. Args: mask (Tensor): ByteTensor mask. Non-zero values representing ignored positions, while zero values means valid positions for this image. Shape [bs, h, w]. Returns: pos (Tensor): Returned position embedding with shape [bs, num_feats*2, h, w]. """ # For convenience of exporting to ONNX, it's required to convert # `masks` from bool to int. mask = mask.to(torch.int) not_mask = 1 - mask # logical_not y_embed = not_mask.cumsum(1, dtype=torch.float32) x_embed = not_mask.cumsum(2, dtype=torch.float32) if self.normalize: y_embed = (y_embed + self.offset) / (y_embed[:, -1:, :] + self.eps) * self.scale x_embed = (x_embed + self.offset) / (x_embed[:, :, -1:] + self.eps) * self.scale dim_t = torch.arange(self.num_feats, dtype=torch.float32, device=mask.device) dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_feats) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t # use `view` instead of `flatten` for dynamically exporting to ONNX B, H, W = mask.size() pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1) pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos def __repr__(self): """str: a string that describes the module""" repr_str = self.__class__.__name__ repr_str += f"(num_feats={self.num_feats}, " repr_str += f"temperature={self.temperature}, " repr_str += f"normalize={self.normalize}, " repr_str += f"scale={self.scale}, " repr_str += f"eps={self.eps})" return repr_str @POSITIONAL_ENCODING.register_module() class LearnedPositionalEncoding(BaseModule): """Position embedding with learnable embedding weights. Args: num_feats (int): The feature dimension for each position along x-axis or y-axis. The final returned dimension for each position is 2 times of this value. row_num_embed (int, optional): The dictionary size of row embeddings. Default 50. col_num_embed (int, optional): The dictionary size of col embeddings. Default 50. init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__(self, num_feats, row_num_embed=50, col_num_embed=50, init_cfg=dict(type="Uniform", layer="Embedding")): super(LearnedPositionalEncoding, self).__init__(init_cfg) self.row_embed = nn.Embedding(row_num_embed, num_feats) self.col_embed = nn.Embedding(col_num_embed, num_feats) self.num_feats = num_feats self.row_num_embed = row_num_embed self.col_num_embed = col_num_embed def forward(self, mask): """Forward function for `LearnedPositionalEncoding`. Args: mask (Tensor): ByteTensor mask. Non-zero values representing ignored positions, while zero values means valid positions for this image. Shape [bs, h, w]. Returns: pos (Tensor): Returned position embedding with shape [bs, num_feats*2, h, w]. """ h, w = mask.shape[-2:] x = torch.arange(w, device=mask.device) y = torch.arange(h, device=mask.device) x_embed = self.col_embed(x) y_embed = self.row_embed(y) pos = ( torch.cat((x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat(1, w, 1)), dim=-1) .permute(2, 0, 1) .unsqueeze(0) .repeat(mask.shape[0], 1, 1, 1) ) return pos def __repr__(self): """str: a string that describes the module""" repr_str = self.__class__.__name__ repr_str += f"(num_feats={self.num_feats}, " repr_str += f"row_num_embed={self.row_num_embed}, " repr_str += f"col_num_embed={self.col_num_embed})" return repr_str ================================================ FILE: dinov2/eval/segmentation_m2f/models/utils/transformer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import math import warnings from typing import Sequence import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as cp from mmcv.cnn import Linear, build_activation_layer, build_norm_layer, xavier_init from mmcv.cnn.bricks.drop import build_dropout from mmcv.cnn.bricks.registry import FEEDFORWARD_NETWORK, TRANSFORMER_LAYER, TRANSFORMER_LAYER_SEQUENCE from mmcv.cnn.bricks.transformer import BaseTransformerLayer, TransformerLayerSequence, build_transformer_layer_sequence from mmcv.runner.base_module import BaseModule, Sequential from mmcv.utils import deprecated_api_warning, to_2tuple from torch.nn.init import normal_ from ..builder import TRANSFORMER try: from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention except ImportError: warnings.warn( "`MultiScaleDeformableAttention` in MMCV has been moved to " "`mmcv.ops.multi_scale_deform_attn`, please update your MMCV" ) from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention class AdaptivePadding(nn.Module): """Applies padding to input (if needed) so that input can get fully covered by filter you specified. It support two modes "same" and "corner". The "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around input. The "corner" mode would pad zero to bottom right. Args: kernel_size (int | tuple): Size of the kernel: stride (int | tuple): Stride of the filter. Default: 1: dilation (int | tuple): Spacing between kernel elements. Default: 1 padding (str): Support "same" and "corner", "corner" mode would pad zero to bottom right, and "same" mode would pad zero around input. Default: "corner". Example: >>> kernel_size = 16 >>> stride = 16 >>> dilation = 1 >>> input = torch.rand(1, 1, 15, 17) >>> adap_pad = AdaptivePadding( >>> kernel_size=kernel_size, >>> stride=stride, >>> dilation=dilation, >>> padding="corner") >>> out = adap_pad(input) >>> assert (out.shape[2], out.shape[3]) == (16, 32) >>> input = torch.rand(1, 1, 16, 17) >>> out = adap_pad(input) >>> assert (out.shape[2], out.shape[3]) == (16, 32) """ def __init__(self, kernel_size=1, stride=1, dilation=1, padding="corner"): super(AdaptivePadding, self).__init__() assert padding in ("same", "corner") kernel_size = to_2tuple(kernel_size) stride = to_2tuple(stride) padding = to_2tuple(padding) dilation = to_2tuple(dilation) self.padding = padding self.kernel_size = kernel_size self.stride = stride self.dilation = dilation def get_pad_shape(self, input_shape): input_h, input_w = input_shape kernel_h, kernel_w = self.kernel_size stride_h, stride_w = self.stride output_h = math.ceil(input_h / stride_h) output_w = math.ceil(input_w / stride_w) pad_h = max((output_h - 1) * stride_h + (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0) pad_w = max((output_w - 1) * stride_w + (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0) return pad_h, pad_w def forward(self, x): pad_h, pad_w = self.get_pad_shape(x.size()[-2:]) if pad_h > 0 or pad_w > 0: if self.padding == "corner": x = F.pad(x, [0, pad_w, 0, pad_h]) elif self.padding == "same": x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]) return x class PatchMerging(BaseModule): """Merge patch feature map. This layer groups feature map by kernel_size, and applies norm and linear layers to the grouped feature map. Our implementation uses `nn.Unfold` to merge patch, which is about 25% faster than original implementation. Instead, we need to modify pretrained models for compatibility. Args: in_channels (int): The num of input channels. to gets fully covered by filter and stride you specified.. Default: True. out_channels (int): The num of output channels. kernel_size (int | tuple, optional): the kernel size in the unfold layer. Defaults to 2. stride (int | tuple, optional): the stride of the sliding blocks in the unfold layer. Default: None. (Would be set as `kernel_size`) padding (int | tuple | string ): The padding length of embedding conv. When it is a string, it means the mode of adaptive padding, support "same" and "corner" now. Default: "corner". dilation (int | tuple, optional): dilation parameter in the unfold layer. Default: 1. bias (bool, optional): Whether to add bias in linear layer or not. Defaults: False. norm_cfg (dict, optional): Config dict for normalization layer. Default: dict(type='LN'). init_cfg (dict, optional): The extra config for initialization. Default: None. """ def __init__( self, in_channels, out_channels, kernel_size=2, stride=None, padding="corner", dilation=1, bias=False, norm_cfg=dict(type="LN"), init_cfg=None, ): super().__init__(init_cfg=init_cfg) self.in_channels = in_channels self.out_channels = out_channels if stride: stride = stride else: stride = kernel_size kernel_size = to_2tuple(kernel_size) stride = to_2tuple(stride) dilation = to_2tuple(dilation) if isinstance(padding, str): self.adap_padding = AdaptivePadding( kernel_size=kernel_size, stride=stride, dilation=dilation, padding=padding ) # disable the padding of unfold padding = 0 else: self.adap_padding = None padding = to_2tuple(padding) self.sampler = nn.Unfold(kernel_size=kernel_size, dilation=dilation, padding=padding, stride=stride) sample_dim = kernel_size[0] * kernel_size[1] * in_channels if norm_cfg is not None: self.norm = build_norm_layer(norm_cfg, sample_dim)[1] else: self.norm = None self.reduction = nn.Linear(sample_dim, out_channels, bias=bias) def forward(self, x, input_size): """ Args: x (Tensor): Has shape (B, H*W, C_in). input_size (tuple[int]): The spatial shape of x, arrange as (H, W). Default: None. Returns: tuple: Contains merged results and its spatial shape. - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out) - out_size (tuple[int]): Spatial shape of x, arrange as (Merged_H, Merged_W). """ B, L, C = x.shape assert isinstance(input_size, Sequence), f"Expect " f"input_size is " f"`Sequence` " f"but get {input_size}" H, W = input_size assert L == H * W, "input feature has wrong size" x = x.view(B, H, W, C).permute([0, 3, 1, 2]) # B, C, H, W # Use nn.Unfold to merge patch. About 25% faster than original method, # but need to modify pretrained model for compatibility if self.adap_padding: x = self.adap_padding(x) H, W = x.shape[-2:] x = self.sampler(x) # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2) out_h = ( H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] * (self.sampler.kernel_size[0] - 1) - 1 ) // self.sampler.stride[0] + 1 out_w = ( W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] * (self.sampler.kernel_size[1] - 1) - 1 ) // self.sampler.stride[1] + 1 output_size = (out_h, out_w) x = x.transpose(1, 2) # B, H/2*W/2, 4*C x = self.norm(x) if self.norm else x x = self.reduction(x) return x, output_size def inverse_sigmoid(x, eps=1e-5): """Inverse function of sigmoid. Args: x (Tensor): The tensor to do the inverse. eps (float): EPS avoid numerical overflow. Defaults 1e-5. Returns: Tensor: The x has passed the inverse function of sigmoid, has same shape with input. """ x = x.clamp(min=0, max=1) x1 = x.clamp(min=eps) x2 = (1 - x).clamp(min=eps) return torch.log(x1 / x2) @FEEDFORWARD_NETWORK.register_module(force=True) class FFN(BaseModule): """Implements feed-forward networks (FFNs) with identity connection. Args: embed_dims (int): The feature dimension. Same as `MultiheadAttention`. Defaults: 256. feedforward_channels (int): The hidden dimension of FFNs. Defaults: 1024. num_fcs (int, optional): The number of fully-connected layers in FFNs. Default: 2. act_cfg (dict, optional): The activation config for FFNs. Default: dict(type='ReLU') ffn_drop (float, optional): Probability of an element to be zeroed in FFN. Default 0.0. add_identity (bool, optional): Whether to add the identity connection. Default: `True`. dropout_layer (obj:`ConfigDict`): The dropout_layer used when adding the shortcut. init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. Default: None. """ @deprecated_api_warning({"dropout": "ffn_drop", "add_residual": "add_identity"}, cls_name="FFN") def __init__( self, embed_dims=256, feedforward_channels=1024, num_fcs=2, act_cfg=dict(type="ReLU", inplace=True), ffn_drop=0.0, dropout_layer=None, add_identity=True, init_cfg=None, with_cp=False, **kwargs, ): super().__init__(init_cfg) assert num_fcs >= 2, "num_fcs should be no less " f"than 2. got {num_fcs}." self.embed_dims = embed_dims self.feedforward_channels = feedforward_channels self.num_fcs = num_fcs self.act_cfg = act_cfg self.activate = build_activation_layer(act_cfg) self.with_cp = with_cp layers = [] in_channels = embed_dims for _ in range(num_fcs - 1): layers.append(Sequential(Linear(in_channels, feedforward_channels), self.activate, nn.Dropout(ffn_drop))) in_channels = feedforward_channels layers.append(Linear(feedforward_channels, embed_dims)) layers.append(nn.Dropout(ffn_drop)) self.layers = Sequential(*layers) self.dropout_layer = build_dropout(dropout_layer) if dropout_layer else torch.nn.Identity() self.add_identity = add_identity @deprecated_api_warning({"residual": "identity"}, cls_name="FFN") def forward(self, x, identity=None): """Forward function for `FFN`. The function would add x to the output tensor if residue is None. """ if self.with_cp and x.requires_grad: out = cp.checkpoint(self.layers, x) else: out = self.layers(x) if not self.add_identity: return self.dropout_layer(out) if identity is None: identity = x return identity + self.dropout_layer(out) @TRANSFORMER_LAYER.register_module() class DetrTransformerDecoderLayer(BaseTransformerLayer): """Implements decoder layer in DETR transformer. Args: attn_cfgs (list[`mmcv.ConfigDict`] | list[dict] | dict )): Configs for self_attention or cross_attention, the order should be consistent with it in `operation_order`. If it is a dict, it would be expand to the number of attention in `operation_order`. feedforward_channels (int): The hidden dimension for FFNs. ffn_dropout (float): Probability of an element to be zeroed in ffn. Default 0.0. operation_order (tuple[str]): The execution order of operation in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm'). Default：None act_cfg (dict): The activation config for FFNs. Default: `LN` norm_cfg (dict): Config dict for normalization layer. Default: `LN`. ffn_num_fcs (int): The number of fully-connected layers in FFNs. Default：2. """ def __init__( self, attn_cfgs, feedforward_channels, ffn_dropout=0.0, operation_order=None, act_cfg=dict(type="ReLU", inplace=True), norm_cfg=dict(type="LN"), ffn_num_fcs=2, **kwargs, ): super(DetrTransformerDecoderLayer, self).__init__( attn_cfgs=attn_cfgs, feedforward_channels=feedforward_channels, ffn_dropout=ffn_dropout, operation_order=operation_order, act_cfg=act_cfg, norm_cfg=norm_cfg, ffn_num_fcs=ffn_num_fcs, **kwargs, ) assert len(operation_order) == 6 assert set(operation_order) == set(["self_attn", "norm", "cross_attn", "ffn"]) @TRANSFORMER_LAYER_SEQUENCE.register_module() class DetrTransformerEncoder(TransformerLayerSequence): """TransformerEncoder of DETR. Args: post_norm_cfg (dict): Config of last normalization layer. Default： `LN`. Only used when `self.pre_norm` is `True` """ def __init__(self, *args, post_norm_cfg=dict(type="LN"), **kwargs): super(DetrTransformerEncoder, self).__init__(*args, **kwargs) if post_norm_cfg is not None: self.post_norm = build_norm_layer(post_norm_cfg, self.embed_dims)[1] if self.pre_norm else None else: assert not self.pre_norm, f"Use prenorm in " f"{self.__class__.__name__}," f"Please specify post_norm_cfg" self.post_norm = None def forward(self, *args, **kwargs): """Forward function for `TransformerCoder`. Returns: Tensor: forwarded results with shape [num_query, bs, embed_dims]. """ x = super(DetrTransformerEncoder, self).forward(*args, **kwargs) if self.post_norm is not None: x = self.post_norm(x) return x @TRANSFORMER_LAYER_SEQUENCE.register_module() class DetrTransformerDecoder(TransformerLayerSequence): """Implements the decoder in DETR transformer. Args: return_intermediate (bool): Whether to return intermediate outputs. post_norm_cfg (dict): Config of last normalization layer. Default： `LN`. """ def __init__(self, *args, post_norm_cfg=dict(type="LN"), return_intermediate=False, **kwargs): super(DetrTransformerDecoder, self).__init__(*args, **kwargs) self.return_intermediate = return_intermediate if post_norm_cfg is not None: self.post_norm = build_norm_layer(post_norm_cfg, self.embed_dims)[1] else: self.post_norm = None def forward(self, query, *args, **kwargs): """Forward function for `TransformerDecoder`. Args: query (Tensor): Input query with shape `(num_query, bs, embed_dims)`. Returns: Tensor: Results with shape [1, num_query, bs, embed_dims] when return_intermediate is `False`, otherwise it has shape [num_layers, num_query, bs, embed_dims]. """ if not self.return_intermediate: x = super().forward(query, *args, **kwargs) if self.post_norm: x = self.post_norm(x)[None] return x intermediate = [] for layer in self.layers: query = layer(query, *args, **kwargs) if self.return_intermediate: if self.post_norm is not None: intermediate.append(self.post_norm(query)) else: intermediate.append(query) return torch.stack(intermediate) @TRANSFORMER.register_module() class Transformer(BaseModule): """Implements the DETR transformer. Following the official DETR implementation, this module copy-paste from torch.nn.Transformer with modifications: * positional encodings are passed in MultiheadAttention * extra LN at the end of encoder is removed * decoder returns a stack of activations from all decoding layers See `paper: End-to-End Object Detection with Transformers `_ for details. Args: encoder (`mmcv.ConfigDict` | Dict): Config of TransformerEncoder. Defaults to None. decoder ((`mmcv.ConfigDict` | Dict)): Config of TransformerDecoder. Defaults to None init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. Defaults to None. """ def __init__(self, encoder=None, decoder=None, init_cfg=None): super(Transformer, self).__init__(init_cfg=init_cfg) self.encoder = build_transformer_layer_sequence(encoder) self.decoder = build_transformer_layer_sequence(decoder) self.embed_dims = self.encoder.embed_dims def init_weights(self): # follow the official DETR to init parameters for m in self.modules(): if hasattr(m, "weight") and m.weight.dim() > 1: xavier_init(m, distribution="uniform") self._is_init = True def forward(self, x, mask, query_embed, pos_embed): """Forward function for `Transformer`. Args: x (Tensor): Input query with shape [bs, c, h, w] where c = embed_dims. mask (Tensor): The key_padding_mask used for encoder and decoder, with shape [bs, h, w]. query_embed (Tensor): The query embedding for decoder, with shape [num_query, c]. pos_embed (Tensor): The positional encoding for encoder and decoder, with the same shape as `x`. Returns: tuple[Tensor]: results of decoder containing the following tensor. - out_dec: Output from decoder. If return_intermediate_dec \ is True output has shape [num_dec_layers, bs, num_query, embed_dims], else has shape [1, bs, \ num_query, embed_dims]. - memory: Output results from encoder, with shape \ [bs, embed_dims, h, w]. """ bs, c, h, w = x.shape # use `view` instead of `flatten` for dynamically exporting to ONNX x = x.view(bs, c, -1).permute(2, 0, 1) # [bs, c, h, w] -> [h*w, bs, c] pos_embed = pos_embed.view(bs, c, -1).permute(2, 0, 1) query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) # [num_query, dim] -> [num_query, bs, dim] mask = mask.view(bs, -1) # [bs, h, w] -> [bs, h*w] memory = self.encoder(query=x, key=None, value=None, query_pos=pos_embed, query_key_padding_mask=mask) target = torch.zeros_like(query_embed) # out_dec: [num_layers, num_query, bs, dim] out_dec = self.decoder( query=target, key=memory, value=memory, key_pos=pos_embed, query_pos=query_embed, key_padding_mask=mask ) out_dec = out_dec.transpose(1, 2) memory = memory.permute(1, 2, 0).reshape(bs, c, h, w) return out_dec, memory @TRANSFORMER_LAYER_SEQUENCE.register_module() class DeformableDetrTransformerDecoder(TransformerLayerSequence): """Implements the decoder in DETR transformer. Args: return_intermediate (bool): Whether to return intermediate outputs. coder_norm_cfg (dict): Config of last normalization layer. Default： `LN`. """ def __init__(self, *args, return_intermediate=False, **kwargs): super(DeformableDetrTransformerDecoder, self).__init__(*args, **kwargs) self.return_intermediate = return_intermediate def forward(self, query, *args, reference_points=None, valid_ratios=None, reg_branches=None, **kwargs): """Forward function for `TransformerDecoder`. Args: query (Tensor): Input query with shape `(num_query, bs, embed_dims)`. reference_points (Tensor): The reference points of offset. has shape (bs, num_query, 4) when as_two_stage, otherwise has shape ((bs, num_query, 2). valid_ratios (Tensor): The radios of valid points on the feature map, has shape (bs, num_levels, 2) reg_branch: (obj:`nn.ModuleList`): Used for refining the regression results. Only would be passed when with_box_refine is True, otherwise would be passed a `None`. Returns: Tensor: Results with shape [1, num_query, bs, embed_dims] when return_intermediate is `False`, otherwise it has shape [num_layers, num_query, bs, embed_dims]. """ output = query intermediate = [] intermediate_reference_points = [] for lid, layer in enumerate(self.layers): if reference_points.shape[-1] == 4: reference_points_input = ( reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None] ) else: assert reference_points.shape[-1] == 2 reference_points_input = reference_points[:, :, None] * valid_ratios[:, None] output = layer(output, *args, reference_points=reference_points_input, **kwargs) output = output.permute(1, 0, 2) if reg_branches is not None: tmp = reg_branches[lid](output) if reference_points.shape[-1] == 4: new_reference_points = tmp + inverse_sigmoid(reference_points) new_reference_points = new_reference_points.sigmoid() else: assert reference_points.shape[-1] == 2 new_reference_points = tmp new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) new_reference_points = new_reference_points.sigmoid() reference_points = new_reference_points.detach() output = output.permute(1, 0, 2) if self.return_intermediate: intermediate.append(output) intermediate_reference_points.append(reference_points) if self.return_intermediate: return torch.stack(intermediate), torch.stack(intermediate_reference_points) return output, reference_points @TRANSFORMER.register_module() class DeformableDetrTransformer(Transformer): """Implements the DeformableDETR transformer. Args: as_two_stage (bool): Generate query from encoder features. Default: False. num_feature_levels (int): Number of feature maps from FPN: Default: 4. two_stage_num_proposals (int): Number of proposals when set `as_two_stage` as True. Default: 300. """ def __init__(self, as_two_stage=False, num_feature_levels=4, two_stage_num_proposals=300, **kwargs): super(DeformableDetrTransformer, self).__init__(**kwargs) self.as_two_stage = as_two_stage self.num_feature_levels = num_feature_levels self.two_stage_num_proposals = two_stage_num_proposals self.embed_dims = self.encoder.embed_dims self.init_layers() def init_layers(self): """Initialize layers of the DeformableDetrTransformer.""" self.level_embeds = nn.Parameter(torch.Tensor(self.num_feature_levels, self.embed_dims)) if self.as_two_stage: self.enc_output = nn.Linear(self.embed_dims, self.embed_dims) self.enc_output_norm = nn.LayerNorm(self.embed_dims) self.pos_trans = nn.Linear(self.embed_dims * 2, self.embed_dims * 2) self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2) else: self.reference_points = nn.Linear(self.embed_dims, 2) def init_weights(self): """Initialize the transformer weights.""" for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) for m in self.modules(): if isinstance(m, MultiScaleDeformableAttention): m.init_weights() if not self.as_two_stage: xavier_init(self.reference_points, distribution="uniform", bias=0.0) normal_(self.level_embeds) def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes): """Generate proposals from encoded memory. Args: memory (Tensor) : The output of encoder, has shape (bs, num_key, embed_dim). num_key is equal the number of points on feature map from all level. memory_padding_mask (Tensor): Padding mask for memory. has shape (bs, num_key). spatial_shapes (Tensor): The shape of all feature maps. has shape (num_level, 2). Returns: tuple: A tuple of feature map and bbox prediction. - output_memory (Tensor): The input of decoder, \ has shape (bs, num_key, embed_dim). num_key is \ equal the number of points on feature map from \ all levels. - output_proposals (Tensor): The normalized proposal \ after a inverse sigmoid, has shape \ (bs, num_keys, 4). """ N, S, C = memory.shape proposals = [] _cur = 0 for lvl, (H, W) in enumerate(spatial_shapes): mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H * W)].view(N, H, W, 1) valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) grid_y, grid_x = torch.meshgrid( torch.linspace(0, H - 1, H, dtype=torch.float32, device=memory.device), torch.linspace(0, W - 1, W, dtype=torch.float32, device=memory.device), ) grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2) grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) proposal = torch.cat((grid, wh), -1).view(N, -1, 4) proposals.append(proposal) _cur += H * W output_proposals = torch.cat(proposals, 1) output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) output_proposals = torch.log(output_proposals / (1 - output_proposals)) output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf")) output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) output_memory = memory output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0)) output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) output_memory = self.enc_output_norm(self.enc_output(output_memory)) return output_memory, output_proposals @staticmethod def get_reference_points(spatial_shapes, valid_ratios, device): """Get the reference points used in decoder. Args: spatial_shapes (Tensor): The shape of all feature maps, has shape (num_level, 2). valid_ratios (Tensor): The radios of valid points on the feature map, has shape (bs, num_levels, 2) device (obj:`device`): The device where reference_points should be. Returns: Tensor: reference points used in decoder, has \ shape (bs, num_keys, num_levels, 2). """ reference_points_list = [] for lvl, (H, W) in enumerate(spatial_shapes): ref_y, ref_x = torch.meshgrid( torch.linspace(0.5, H - 0.5, H, dtype=torch.float32, device=device), torch.linspace(0.5, W - 0.5, W, dtype=torch.float32, device=device), ) ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H) ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W) ref = torch.stack((ref_x, ref_y), -1) reference_points_list.append(ref) reference_points = torch.cat(reference_points_list, 1) reference_points = reference_points[:, :, None] * valid_ratios[:, None] return reference_points def get_valid_ratio(self, mask): """Get the valid radios of feature maps of all level.""" _, H, W = mask.shape valid_H = torch.sum(~mask[:, :, 0], 1) valid_W = torch.sum(~mask[:, 0, :], 1) valid_ratio_h = valid_H.float() / H valid_ratio_w = valid_W.float() / W valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) return valid_ratio def get_proposal_pos_embed(self, proposals, num_pos_feats=128, temperature=10000): """Get the position embedding of proposal.""" scale = 2 * math.pi dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device) dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats) # N, L, 4 proposals = proposals.sigmoid() * scale # N, L, 4, 128 pos = proposals[:, :, :, None] / dim_t # N, L, 4, 64, 2 pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) return pos def forward( self, mlvl_feats, mlvl_masks, query_embed, mlvl_pos_embeds, reg_branches=None, cls_branches=None, **kwargs ): """Forward function for `Transformer`. Args: mlvl_feats (list(Tensor)): Input queries from different level. Each element has shape [bs, embed_dims, h, w]. mlvl_masks (list(Tensor)): The key_padding_mask from different level used for encoder and decoder, each element has shape [bs, h, w]. query_embed (Tensor): The query embedding for decoder, with shape [num_query, c]. mlvl_pos_embeds (list(Tensor)): The positional encoding of feats from different level, has the shape [bs, embed_dims, h, w]. reg_branches (obj:`nn.ModuleList`): Regression heads for feature maps from each decoder layer. Only would be passed when `with_box_refine` is True. Default to None. cls_branches (obj:`nn.ModuleList`): Classification heads for feature maps from each decoder layer. Only would be passed when `as_two_stage` is True. Default to None. Returns: tuple[Tensor]: results of decoder containing the following tensor. - inter_states: Outputs from decoder. If return_intermediate_dec is True output has shape \ (num_dec_layers, bs, num_query, embed_dims), else has \ shape (1, bs, num_query, embed_dims). - init_reference_out: The initial value of reference \ points, has shape (bs, num_queries, 4). - inter_references_out: The internal value of reference \ points in decoder, has shape \ (num_dec_layers, bs,num_query, embed_dims) - enc_outputs_class: The classification score of \ proposals generated from \ encoder's feature maps, has shape \ (batch, h*w, num_classes). \ Only would be returned when `as_two_stage` is True, \ otherwise None. - enc_outputs_coord_unact: The regression results \ generated from encoder's feature maps., has shape \ (batch, h*w, 4). Only would \ be returned when `as_two_stage` is True, \ otherwise None. """ assert self.as_two_stage or query_embed is not None feat_flatten = [] mask_flatten = [] lvl_pos_embed_flatten = [] spatial_shapes = [] for lvl, (feat, mask, pos_embed) in enumerate(zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)): bs, c, h, w = feat.shape spatial_shape = (h, w) spatial_shapes.append(spatial_shape) feat = feat.flatten(2).transpose(1, 2) mask = mask.flatten(1) pos_embed = pos_embed.flatten(2).transpose(1, 2) lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1) lvl_pos_embed_flatten.append(lvl_pos_embed) feat_flatten.append(feat) mask_flatten.append(mask) feat_flatten = torch.cat(feat_flatten, 1) mask_flatten = torch.cat(mask_flatten, 1) lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=feat_flatten.device) level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) valid_ratios = torch.stack([self.get_valid_ratio(m) for m in mlvl_masks], 1) reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=feat.device) feat_flatten = feat_flatten.permute(1, 0, 2) # (H*W, bs, embed_dims) lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute(1, 0, 2) # (H*W, bs, embed_dims) memory = self.encoder( query=feat_flatten, key=None, value=None, query_pos=lvl_pos_embed_flatten, query_key_padding_mask=mask_flatten, spatial_shapes=spatial_shapes, reference_points=reference_points, level_start_index=level_start_index, valid_ratios=valid_ratios, **kwargs, ) memory = memory.permute(1, 0, 2) bs, _, c = memory.shape if self.as_two_stage: output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes) enc_outputs_class = cls_branches[self.decoder.num_layers](output_memory) enc_outputs_coord_unact = reg_branches[self.decoder.num_layers](output_memory) + output_proposals topk = self.two_stage_num_proposals topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) topk_coords_unact = topk_coords_unact.detach() reference_points = topk_coords_unact.sigmoid() init_reference_out = reference_points pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact))) query_pos, query = torch.split(pos_trans_out, c, dim=2) else: query_pos, query = torch.split(query_embed, c, dim=1) query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1) query = query.unsqueeze(0).expand(bs, -1, -1) reference_points = self.reference_points(query_pos).sigmoid() init_reference_out = reference_points # decoder query = query.permute(1, 0, 2) memory = memory.permute(1, 0, 2) query_pos = query_pos.permute(1, 0, 2) inter_states, inter_references = self.decoder( query=query, key=None, value=memory, query_pos=query_pos, key_padding_mask=mask_flatten, reference_points=reference_points, spatial_shapes=spatial_shapes, level_start_index=level_start_index, valid_ratios=valid_ratios, reg_branches=reg_branches, **kwargs, ) inter_references_out = inter_references if self.as_two_stage: return inter_states, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact return inter_states, init_reference_out, inter_references_out, None, None @TRANSFORMER.register_module() class DynamicConv(BaseModule): """Implements Dynamic Convolution. This module generate parameters for each sample and use bmm to implement 1*1 convolution. Code is modified from the `official github repo `_ . Args: in_channels (int): The input feature channel. Defaults to 256. feat_channels (int): The inner feature channel. Defaults to 64. out_channels (int, optional): The output feature channel. When not specified, it will be set to `in_channels` by default input_feat_shape (int): The shape of input feature. Defaults to 7. with_proj (bool): Project two-dimentional feature to one-dimentional feature. Default to True. act_cfg (dict): The activation config for DynamicConv. norm_cfg (dict): Config dict for normalization layer. Default layer normalization. init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. Default: None. """ def __init__( self, in_channels=256, feat_channels=64, out_channels=None, input_feat_shape=7, with_proj=True, act_cfg=dict(type="ReLU", inplace=True), norm_cfg=dict(type="LN"), init_cfg=None, ): super(DynamicConv, self).__init__(init_cfg) self.in_channels = in_channels self.feat_channels = feat_channels self.out_channels_raw = out_channels self.input_feat_shape = input_feat_shape self.with_proj = with_proj self.act_cfg = act_cfg self.norm_cfg = norm_cfg self.out_channels = out_channels if out_channels else in_channels self.num_params_in = self.in_channels * self.feat_channels self.num_params_out = self.out_channels * self.feat_channels self.dynamic_layer = nn.Linear(self.in_channels, self.num_params_in + self.num_params_out) self.norm_in = build_norm_layer(norm_cfg, self.feat_channels)[1] self.norm_out = build_norm_layer(norm_cfg, self.out_channels)[1] self.activation = build_activation_layer(act_cfg) num_output = self.out_channels * input_feat_shape**2 if self.with_proj: self.fc_layer = nn.Linear(num_output, self.out_channels) self.fc_norm = build_norm_layer(norm_cfg, self.out_channels)[1] def forward(self, param_feature, input_feature): """Forward function for `DynamicConv`. Args: param_feature (Tensor): The feature can be used to generate the parameter, has shape (num_all_proposals, in_channels). input_feature (Tensor): Feature that interact with parameters, has shape (num_all_proposals, in_channels, H, W). Returns: Tensor: The output feature has shape (num_all_proposals, out_channels). """ input_feature = input_feature.flatten(2).permute(2, 0, 1) input_feature = input_feature.permute(1, 0, 2) parameters = self.dynamic_layer(param_feature) param_in = parameters[:, : self.num_params_in].view(-1, self.in_channels, self.feat_channels) param_out = parameters[:, -self.num_params_out :].view(-1, self.feat_channels, self.out_channels) # input_feature has shape (num_all_proposals, H*W, in_channels) # param_in has shape (num_all_proposals, in_channels, feat_channels) # feature has shape (num_all_proposals, H*W, feat_channels) features = torch.bmm(input_feature, param_in) features = self.norm_in(features) features = self.activation(features) # param_out has shape (batch_size, feat_channels, out_channels) features = torch.bmm(features, param_out) features = self.norm_out(features) features = self.activation(features) if self.with_proj: features = features.flatten(1) features = self.fc_layer(features) features = self.fc_norm(features) features = self.activation(features) return features ================================================ FILE: dinov2/eval/segmentation_m2f/ops/modules/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/fundamentalvision/Deformable-DETR/tree/main/models/ops/modules # https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 from .ms_deform_attn import MSDeformAttn ================================================ FILE: dinov2/eval/segmentation_m2f/ops/modules/ms_deform_attn.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import math import warnings import torch import torch.nn.functional as F from torch import nn from torch.autograd import Function from torch.cuda.amp import custom_fwd from torch.nn.init import constant_, xavier_uniform_ class MSDeformAttnFunction(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward( ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step ): output = ms_deform_attn_core_pytorch( value, value_spatial_shapes, # value_level_start_index, sampling_locations, attention_weights, ) return output def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): # for debug and test only, # need to use cuda version instead N_, S_, M_, D_ = value.shape _, Lq_, M_, L_, P_, _ = sampling_locations.shape value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) sampling_grids = 2 * sampling_locations - 1 sampling_value_list = [] for lid_, (H_, W_) in enumerate(value_spatial_shapes): # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_ * M_, D_, H_, W_) # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) # N_*M_, D_, Lq_, P_ sampling_value_l_ = F.grid_sample( value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False ) sampling_value_list.append(sampling_value_l_) # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) attention_weights = attention_weights.transpose(1, 2).reshape(N_ * M_, 1, Lq_, L_ * P_) output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_ * D_, Lq_) return output.transpose(1, 2).contiguous() def _is_power_of_2(n): if (not isinstance(n, int)) or (n < 0): raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) return (n & (n - 1) == 0) and n != 0 class MSDeformAttn(nn.Module): def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4, ratio=1.0): """Multi-Scale Deformable Attention Module. :param d_model hidden dimension :param n_levels number of feature levels :param n_heads number of attention heads :param n_points number of sampling points per attention head per feature level """ super().__init__() if d_model % n_heads != 0: raise ValueError("d_model must be divisible by n_heads, " "but got {} and {}".format(d_model, n_heads)) _d_per_head = d_model // n_heads # you'd better set _d_per_head to a power of 2 # which is more efficient in our CUDA implementation if not _is_power_of_2(_d_per_head): warnings.warn( "You'd better set d_model in MSDeformAttn to make " "the dimension of each attention head a power of 2 " "which is more efficient in our CUDA implementation." ) self.im2col_step = 64 self.d_model = d_model self.n_levels = n_levels self.n_heads = n_heads self.n_points = n_points self.ratio = ratio self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) self.value_proj = nn.Linear(d_model, int(d_model * ratio)) self.output_proj = nn.Linear(int(d_model * ratio), d_model) self._reset_parameters() def _reset_parameters(self): constant_(self.sampling_offsets.weight.data, 0.0) thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) grid_init = ( (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) .view(self.n_heads, 1, 1, 2) .repeat(1, self.n_levels, self.n_points, 1) ) for i in range(self.n_points): grid_init[:, :, i, :] *= i + 1 with torch.no_grad(): self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) constant_(self.attention_weights.weight.data, 0.0) constant_(self.attention_weights.bias.data, 0.0) xavier_uniform_(self.value_proj.weight.data) constant_(self.value_proj.bias.data, 0.0) xavier_uniform_(self.output_proj.weight.data) constant_(self.output_proj.bias.data, 0.0) def forward( self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None, ): """ :param query (N, Length_{query}, C) :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes :param input_flatten (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l, C) :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] :param input_padding_mask (N, \\sum_{l=0}^{L-1} H_l \\cdot W_l), True for padding elements, False for non-padding elements :return output (N, Length_{query}, C) """ # print(query.shape) # print(reference_points.shape) # print(input_flatten.shape) # print(input_spatial_shapes.shape) # print(input_level_start_index.shape) # print(input_spatial_shapes) # print(input_level_start_index) N, Len_q, _ = query.shape N, Len_in, _ = input_flatten.shape assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in value = self.value_proj(input_flatten) if input_padding_mask is not None: value = value.masked_fill(input_padding_mask[..., None], float(0)) value = value.view(N, Len_in, self.n_heads, int(self.ratio * self.d_model) // self.n_heads) sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) if reference_points.shape[-1] == 2: offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) sampling_locations = ( reference_points[:, :, None, :, None, :] + sampling_offsets / offset_normalizer[None, None, None, :, None, :] ) elif reference_points.shape[-1] == 4: sampling_locations = ( reference_points[:, :, None, :, None, :2] + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 ) else: raise ValueError( "Last dim of reference_points must be 2 or 4, but get {} instead.".format(reference_points.shape[-1]) ) output = MSDeformAttnFunction.apply( value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step, ) output = self.output_proj(output) return output ================================================ FILE: dinov2/eval/setup.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import argparse from typing import Any, List, Optional, Tuple import torch import torch.backends.cudnn as cudnn from dinov2.models import build_model_from_cfg from dinov2.utils.config import setup import dinov2.utils.utils as dinov2_utils def get_args_parser( description: Optional[str] = None, parents: Optional[List[argparse.ArgumentParser]] = None, add_help: bool = True, ): parser = argparse.ArgumentParser( description=description, parents=parents or [], add_help=add_help, ) parser.add_argument( "--config-file", type=str, help="Model configuration file", ) parser.add_argument( "--pretrained-weights", type=str, help="Pretrained model weights", ) parser.add_argument( "--output-dir", default="", type=str, help="Output directory to write results and logs", ) parser.add_argument( "--opts", help="Extra configuration options", default=[], nargs="+", ) return parser def get_autocast_dtype(config): teacher_dtype_str = config.compute_precision.teacher.backbone.mixed_precision.param_dtype if teacher_dtype_str == "fp16": return torch.half elif teacher_dtype_str == "bf16": return torch.bfloat16 else: return torch.float def build_model_for_eval(config, pretrained_weights): model, _ = build_model_from_cfg(config, only_teacher=True) dinov2_utils.load_pretrained_weights(model, pretrained_weights, "teacher") model.eval() model.cuda() return model def setup_and_build_model(args) -> Tuple[Any, torch.dtype]: cudnn.benchmark = True config = setup(args) model = build_model_for_eval(config, args.pretrained_weights) autocast_dtype = get_autocast_dtype(config) return model, autocast_dtype ================================================ FILE: dinov2/eval/utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging from typing import Dict, Optional import torch from torch import nn from torchmetrics import MetricCollection from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader import dinov2.distributed as distributed from dinov2.logging import MetricLogger logger = logging.getLogger("dinov2") class ModelWithNormalize(torch.nn.Module): def __init__(self, model): super().__init__() self.model = model def forward(self, samples): return nn.functional.normalize(self.model(samples), dim=1, p=2) class ModelWithIntermediateLayers(nn.Module): def __init__(self, feature_model, n_last_blocks, autocast_ctx): super().__init__() self.feature_model = feature_model self.feature_model.eval() self.n_last_blocks = n_last_blocks self.autocast_ctx = autocast_ctx def forward(self, images): with torch.inference_mode(): with self.autocast_ctx(): features = self.feature_model.get_intermediate_layers( images, self.n_last_blocks, return_class_token=True ) return features @torch.inference_mode() def evaluate( model: nn.Module, data_loader, postprocessors: Dict[str, nn.Module], metrics: Dict[str, MetricCollection], device: torch.device, criterion: Optional[nn.Module] = None, ): model.eval() if criterion is not None: criterion.eval() for metric in metrics.values(): metric = metric.to(device) metric_logger = MetricLogger(delimiter=" ") header = "Test:" for samples, targets, *_ in metric_logger.log_every(data_loader, 10, header): outputs = model(samples.to(device)) targets = targets.to(device) if criterion is not None: loss = criterion(outputs, targets) metric_logger.update(loss=loss.item()) for k, metric in metrics.items(): metric_inputs = postprocessors[k](outputs, targets) metric.update(**metric_inputs) metric_logger.synchronize_between_processes() logger.info(f"Averaged stats: {metric_logger}") stats = {k: metric.compute() for k, metric in metrics.items()} metric_logger_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} return metric_logger_stats, stats def all_gather_and_flatten(tensor_rank): tensor_all_ranks = torch.empty( distributed.get_global_size(), *tensor_rank.shape, dtype=tensor_rank.dtype, device=tensor_rank.device, ) tensor_list = list(tensor_all_ranks.unbind(0)) torch.distributed.all_gather(tensor_list, tensor_rank.contiguous()) return tensor_all_ranks.flatten(end_dim=1) def extract_features(model, dataset, batch_size, num_workers, gather_on_cpu=False): dataset_with_enumerated_targets = DatasetWithEnumeratedTargets(dataset) sample_count = len(dataset_with_enumerated_targets) data_loader = make_data_loader( dataset=dataset_with_enumerated_targets, batch_size=batch_size, num_workers=num_workers, sampler_type=SamplerType.DISTRIBUTED, drop_last=False, shuffle=False, ) return extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu) @torch.inference_mode() def extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu=False): gather_device = torch.device("cpu") if gather_on_cpu else torch.device("cuda") metric_logger = MetricLogger(delimiter=" ") features, all_labels = None, None for samples, (index, labels_rank) in metric_logger.log_every(data_loader, 10): samples = samples.cuda(non_blocking=True) labels_rank = labels_rank.cuda(non_blocking=True) index = index.cuda(non_blocking=True) features_rank = model(samples).float() # init storage feature matrix if features is None: features = torch.zeros(sample_count, features_rank.shape[-1], device=gather_device) labels_shape = list(labels_rank.shape) labels_shape[0] = sample_count all_labels = torch.full(labels_shape, fill_value=-1, device=gather_device) logger.info(f"Storing features into tensor of shape {features.shape}") # share indexes, features and labels between processes index_all = all_gather_and_flatten(index).to(gather_device) features_all_ranks = all_gather_and_flatten(features_rank).to(gather_device) labels_all_ranks = all_gather_and_flatten(labels_rank).to(gather_device) # update storage feature matrix if len(index_all) > 0: features.index_copy_(0, index_all, features_all_ranks) all_labels.index_copy_(0, index_all, labels_all_ranks) logger.info(f"Features shape: {tuple(features.shape)}") logger.info(f"Labels shape: {tuple(all_labels.shape)}") assert torch.all(all_labels > -1) return features, all_labels ================================================ FILE: dinov2/fsdp/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import os from typing import Any import torch import dinov2.distributed as distributed from functools import partial from fvcore.common.checkpoint import Checkpointer from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp import ShardingStrategy from torch.distributed.fsdp import MixedPrecision from torch.distributed.fsdp import StateDictType from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler from torch.distributed.fsdp.wrap import ModuleWrapPolicy from torch.distributed.fsdp._runtime_utils import _reshard def get_fsdp_wrapper(model_cfg, modules_to_wrap=set()): sharding_strategy_dict = { "NO_SHARD": ShardingStrategy.NO_SHARD, "SHARD_GRAD_OP": ShardingStrategy.SHARD_GRAD_OP, "FULL_SHARD": ShardingStrategy.FULL_SHARD, } dtype_dict = { "fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16, } mixed_precision_config = MixedPrecision( param_dtype=dtype_dict[model_cfg.mixed_precision.param_dtype], reduce_dtype=dtype_dict[model_cfg.mixed_precision.reduce_dtype], buffer_dtype=dtype_dict[model_cfg.mixed_precision.buffer_dtype], ) sharding_strategy_config = sharding_strategy_dict[model_cfg.sharding_strategy] local_rank = distributed.get_local_rank() fsdp_wrapper = partial( FSDP, sharding_strategy=sharding_strategy_config, mixed_precision=mixed_precision_config, device_id=local_rank, sync_module_states=True, use_orig_params=True, auto_wrap_policy=ModuleWrapPolicy(modules_to_wrap), ) return fsdp_wrapper def is_fsdp(x): return isinstance(x, FSDP) def is_sharded_fsdp(x): return is_fsdp(x) and x.sharding_strategy is not ShardingStrategy.NO_SHARD def free_if_fsdp(x): if is_sharded_fsdp(x): handles = x._handles true_list = [True for h in handles] _reshard(x, handles, true_list) def get_fsdp_modules(x): return FSDP.fsdp_modules(x) def reshard_fsdp_model(x): for m in get_fsdp_modules(x): free_if_fsdp(m) def rankstr(): return f"rank_{distributed.get_global_rank()}" class FSDPCheckpointer(Checkpointer): def save(self, name: str, **kwargs: Any) -> None: """ Dump model and checkpointables to a file. Args: name (str): name of the file. kwargs (dict): extra arbitrary data to save. """ if not self.save_dir or not self.save_to_disk: return data = {} with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT): data["model"] = self.model.state_dict() # data["model"] = self.model.state_dict() for key, obj in self.checkpointables.items(): data[key] = obj.state_dict() data.update(kwargs) basename = f"{name}.{rankstr()}.pth" save_file = os.path.join(self.save_dir, basename) assert os.path.basename(save_file) == basename, basename self.logger.info("Saving checkpoint to {}".format(save_file)) with self.path_manager.open(save_file, "wb") as f: torch.save(data, f) self.tag_last_checkpoint(basename) def load(self, *args, **kwargs): with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT): return super().load(*args, **kwargs) def has_checkpoint(self) -> bool: """ Returns: bool: whether a checkpoint exists in the target directory. """ save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}") return self.path_manager.exists(save_file) def get_checkpoint_file(self) -> str: """ Returns: str: The latest checkpoint file in target directory. """ save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}") try: with self.path_manager.open(save_file, "r") as f: last_saved = f.read().strip() except IOError: # if file doesn't exist, maybe because it has just been # deleted by a separate process return "" # pyre-fixme[6]: For 2nd param expected `Union[PathLike[str], str]` but got # `Union[bytes, str]`. return os.path.join(self.save_dir, last_saved) def tag_last_checkpoint(self, last_filename_basename: str) -> None: """ Tag the last checkpoint. Args: last_filename_basename (str): the basename of the last filename. """ if distributed.is_enabled(): torch.distributed.barrier() save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}") with self.path_manager.open(save_file, "w") as f: f.write(last_filename_basename) # pyre-ignore ShardedGradScaler = ShardedGradScaler ================================================ FILE: dinov2/hub/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. ================================================ FILE: dinov2/hub/backbones.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from enum import Enum from pathlib import Path from typing import Optional, Union from urllib.parse import urlparse import torch from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name class Weights(Enum): LVD142M = "LVD142M" XRAY_DINO = "XRay-DINO" def is_url(path: str) -> bool: parsed = urlparse(path) return parsed.scheme in ("https", "file") def convert_path_or_url_to_url(path: str) -> str: if is_url(path): return path return Path(path).expanduser().resolve().as_uri() def _make_dinov2_model( *, arch_name: str = "vit_large", img_size: int = 518, patch_size: int = 14, init_values: float = 1.0, ffn_layer: str = "mlp", block_chunks: int = 0, num_register_tokens: int = 0, interpolate_antialias: bool = False, interpolate_offset: float = 0.1, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, hash: Optional[str] = None, check_hash: bool = False, **kwargs, ): from ..models import vision_transformer as vits model_base_name = _make_dinov2_model_name(arch_name, patch_size) vit_kwargs = dict( img_size=img_size, patch_size=patch_size, init_values=init_values, ffn_layer=ffn_layer, block_chunks=block_chunks, num_register_tokens=num_register_tokens, interpolate_antialias=interpolate_antialias, interpolate_offset=interpolate_offset, ) vit_kwargs.update(**kwargs) model = vits.__dict__[arch_name](**vit_kwargs) if pretrained: if type(weights) is Weights and weights not in { Weights.LVD142M, Weights.XRAY_DINO, }: raise ValueError(f"Unsupported weights for the backbone: {weights}") elif type(weights) is Weights: model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens) url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_pretrain.pth" else: url = convert_path_or_url_to_url(weights) state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu", check_hash=check_hash) model.load_state_dict(state_dict, strict=True) return model def dinov2_vits14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs): """ DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset. """ return _make_dinov2_model(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs) def dinov2_vitb14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs): """ DINOv2 ViT-B/14 model (optionally) pretrained on the LVD-142M dataset. """ return _make_dinov2_model(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs) def dinov2_vitl14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs): """ DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset. """ return _make_dinov2_model(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs) def dinov2_vitg14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs): """ DINOv2 ViT-g/14 model (optionally) pretrained on the LVD-142M dataset. """ return _make_dinov2_model( arch_name="vit_giant2", ffn_layer="swiglufused", weights=weights, pretrained=pretrained, **kwargs, ) def dinov2_vits14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs): """ DINOv2 ViT-S/14 model with registers (optionally) pretrained on the LVD-142M dataset. """ return _make_dinov2_model( arch_name="vit_small", pretrained=pretrained, weights=weights, num_register_tokens=4, interpolate_antialias=True, interpolate_offset=0.0, **kwargs, ) def dinov2_vitb14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs): """ DINOv2 ViT-B/14 model with registers (optionally) pretrained on the LVD-142M dataset. """ return _make_dinov2_model( arch_name="vit_base", pretrained=pretrained, weights=weights, num_register_tokens=4, interpolate_antialias=True, interpolate_offset=0.0, **kwargs, ) def dinov2_vitl14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs): """ DINOv2 ViT-L/14 model with registers (optionally) pretrained on the LVD-142M dataset. """ return _make_dinov2_model( arch_name="vit_large", pretrained=pretrained, weights=weights, num_register_tokens=4, interpolate_antialias=True, interpolate_offset=0.0, **kwargs, ) def dinov2_vitg14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs): """ DINOv2 ViT-g/14 model with registers (optionally) pretrained on the LVD-142M dataset. """ return _make_dinov2_model( arch_name="vit_giant2", ffn_layer="swiglufused", weights=weights, pretrained=pretrained, num_register_tokens=4, interpolate_antialias=True, interpolate_offset=0.0, **kwargs, ) ================================================ FILE: dinov2/hub/cell_dino/backbones.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. from enum import Enum from typing import Optional, Union import torch class Weights(Enum): CELL_DINO = "CELL-DINO" def _make_cell_dino_model( *, arch_name: str = "vit_large", img_size: int = 518, patch_size: int = 14, init_values: float = 1.0, ffn_layer: str = "mlp", block_chunks: int = 0, num_register_tokens: int = 0, interpolate_antialias: bool = False, interpolate_offset: float = 0.1, pretrained: bool = True, channel_adaptive: bool = False, weights: Union[Weights, str] = Weights.CELL_DINO, pretrained_url: Optional[str] = None, pretrained_path: Optional[str] = None, **kwargs, ): from ...models import vision_transformer as vits if isinstance(weights, str): try: weights = Weights[weights] except KeyError: raise AssertionError(f"Unsupported weights: {weights}") vit_kwargs = dict( img_size=img_size, patch_size=patch_size, init_values=init_values, ffn_layer=ffn_layer, block_chunks=block_chunks, num_register_tokens=num_register_tokens, interpolate_antialias=interpolate_antialias, interpolate_offset=interpolate_offset, channel_adaptive=channel_adaptive, ) vit_kwargs.update(**kwargs) model = vits.__dict__[arch_name](**vit_kwargs) if pretrained: if pretrained_path is not None: state_dict = torch.load(pretrained_path, map_location="cpu") else: pretrained_url is not None state_dict = torch.hub.load_state_dict_from_url(pretrained_url, map_location="cpu") model.load_state_dict(state_dict, strict=True) return model def cell_dino_hpa_vitl16( *, pretrained_url: Optional[str] = None, pretrained_path: Optional[str] = None, pretrained: bool = True, weights: Union[Weights, str] = Weights.CELL_DINO, in_channels: int = 4, **kwargs, ): """ Cell-DINO ViT-L/16 model dataset pretrained on HPA dataset. """ return _make_cell_dino_model( arch_name="vit_large", patch_size=16, img_size=224, num_register_tokens=0, interpolate_antialias=False, interpolate_offset=0.1, block_chunks=4, pretrained_url=pretrained_url, pretrained_path=pretrained_path, pretrained=pretrained, weights=weights, in_chans=in_channels, **kwargs, ) def cell_dino_hpa_vitl14( *, pretrained_url: Optional[str] = None, pretrained_path: Optional[str] = None, pretrained: bool = True, weights: Union[Weights, str] = Weights.CELL_DINO, in_channels: int = 4, **kwargs, ): """ Cell-DINO ViT-L/14 model dataset pretrained on LVD, then on HPA dataset. """ return _make_cell_dino_model( arch_name="vit_large", patch_size=14, img_size=518, num_register_tokens=0, interpolate_antialias=False, interpolate_offset=0.1, block_chunks=4, pretrained_url=pretrained_url, pretrained_path=pretrained_path, pretrained=pretrained, weights=weights, in_chans=in_channels, **kwargs, ) def cell_dino_cp_vits8( *, pretrained_url: Optional[str] = None, pretrained_path: Optional[str] = None, pretrained: bool = True, weights: Union[Weights, str] = Weights.CELL_DINO, in_channels: int = 5, **kwargs, ): """ Cell-DINO ViT-S/8 model dataset pretrained on the combined cell painting dataset. """ return _make_cell_dino_model( arch_name="vit_small", patch_size=8, img_size=128, num_register_tokens=0, interpolate_antialias=False, interpolate_offset=0.1, block_chunks=4, pretrained_url=pretrained_url, pretrained_path=pretrained_path, pretrained=pretrained, weights=weights, in_chans=in_channels, **kwargs, ) def channel_adaptive_dino_vitl16( *, pretrained_url: Optional[str] = None, pretrained_path: Optional[str] = None, pretrained: bool = True, weights: Union[Weights, str] = Weights.CELL_DINO, in_channels: int = 1, channel_adaptive: bool = True, **kwargs, ): """ Cell-DINO ViT-L/16 model dataset pretrained on HPA dataset. """ return _make_cell_dino_model( arch_name="vit_large", patch_size=16, img_size=224, num_register_tokens=0, interpolate_antialias=False, interpolate_offset=0.1, block_chunks=4, pretrained_url=pretrained_url, pretrained_path=pretrained_path, pretrained=pretrained, weights=weights, in_chans=in_channels, channel_adaptive=channel_adaptive, **kwargs, ) ================================================ FILE: dinov2/hub/classifiers.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from enum import Enum from typing import Union import torch import torch.nn as nn from .backbones import _make_dinov2_model from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name class Weights(Enum): IMAGENET1K = "IMAGENET1K" def _make_dinov2_linear_classification_head( *, arch_name: str = "vit_large", patch_size: int = 14, embed_dim: int = 1024, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, num_register_tokens: int = 0, **kwargs, ): if layers not in (1, 4): raise AssertionError(f"Unsupported number of layers: {layers}") if isinstance(weights, str): try: weights = Weights[weights] except KeyError: raise AssertionError(f"Unsupported weights: {weights}") linear_head = nn.Linear((1 + layers) * embed_dim, 1_000) if pretrained: model_base_name = _make_dinov2_model_name(arch_name, patch_size) model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens) layers_str = str(layers) if layers == 4 else "" url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_linear{layers_str}_head.pth" state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu") linear_head.load_state_dict(state_dict, strict=True) return linear_head class _LinearClassifierWrapper(nn.Module): def __init__(self, *, backbone: nn.Module, linear_head: nn.Module, layers: int = 4): super().__init__() self.backbone = backbone self.linear_head = linear_head self.layers = layers def forward(self, x): if self.layers == 1: x = self.backbone.forward_features(x) cls_token = x["x_norm_clstoken"] patch_tokens = x["x_norm_patchtokens"] # fmt: off linear_input = torch.cat([ cls_token, patch_tokens.mean(dim=1), ], dim=1) # fmt: on elif self.layers == 4: x = self.backbone.get_intermediate_layers(x, n=4, return_class_token=True) # fmt: off linear_input = torch.cat([ x[0][1], x[1][1], x[2][1], x[3][1], x[3][0].mean(dim=1), ], dim=1) # fmt: on else: assert False, f"Unsupported number of layers: {self.layers}" return self.linear_head(linear_input) def _make_dinov2_linear_classifier( *, arch_name: str = "vit_large", layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, num_register_tokens: int = 0, interpolate_antialias: bool = False, interpolate_offset: float = 0.1, **kwargs, ): backbone = _make_dinov2_model( arch_name=arch_name, pretrained=pretrained, num_register_tokens=num_register_tokens, interpolate_antialias=interpolate_antialias, interpolate_offset=interpolate_offset, **kwargs, ) embed_dim = backbone.embed_dim patch_size = backbone.patch_size linear_head = _make_dinov2_linear_classification_head( arch_name=arch_name, patch_size=patch_size, embed_dim=embed_dim, layers=layers, pretrained=pretrained, weights=weights, num_register_tokens=num_register_tokens, ) return _LinearClassifierWrapper(backbone=backbone, linear_head=linear_head, layers=layers) def dinov2_vits14_lc( *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs, ): """ Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. """ return _make_dinov2_linear_classifier( arch_name="vit_small", layers=layers, pretrained=pretrained, weights=weights, **kwargs, ) def dinov2_vitb14_lc( *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs, ): """ Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. """ return _make_dinov2_linear_classifier( arch_name="vit_base", layers=layers, pretrained=pretrained, weights=weights, **kwargs, ) def dinov2_vitl14_lc( *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs, ): """ Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. """ return _make_dinov2_linear_classifier( arch_name="vit_large", layers=layers, pretrained=pretrained, weights=weights, **kwargs, ) def dinov2_vitg14_lc( *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs, ): """ Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. """ return _make_dinov2_linear_classifier( arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs, ) def dinov2_vits14_reg_lc( *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs ): """ Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. """ return _make_dinov2_linear_classifier( arch_name="vit_small", layers=layers, pretrained=pretrained, weights=weights, num_register_tokens=4, interpolate_antialias=True, interpolate_offset=0.0, **kwargs, ) def dinov2_vitb14_reg_lc( *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs ): """ Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. """ return _make_dinov2_linear_classifier( arch_name="vit_base", layers=layers, pretrained=pretrained, weights=weights, num_register_tokens=4, interpolate_antialias=True, interpolate_offset=0.0, **kwargs, ) def dinov2_vitl14_reg_lc( *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs ): """ Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. """ return _make_dinov2_linear_classifier( arch_name="vit_large", layers=layers, pretrained=pretrained, weights=weights, num_register_tokens=4, interpolate_antialias=True, interpolate_offset=0.0, **kwargs, ) def dinov2_vitg14_reg_lc( *, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.IMAGENET1K, **kwargs ): """ Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone with registers (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. """ return _make_dinov2_linear_classifier( arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, weights=weights, num_register_tokens=4, interpolate_antialias=True, interpolate_offset=0.0, **kwargs, ) ================================================ FILE: dinov2/hub/depth/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .decode_heads import BNHead, DPTHead from .encoder_decoder import DepthEncoderDecoder ================================================ FILE: dinov2/hub/depth/decode_heads.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import copy from functools import partial import math import warnings import torch import torch.nn as nn from .ops import resize # XXX: (Untested) replacement for mmcv.imdenormalize() def _imdenormalize(img, mean, std, to_bgr=True): import numpy as np mean = mean.reshape(1, -1).astype(np.float64) std = std.reshape(1, -1).astype(np.float64) img = (img * std) + mean if to_bgr: img = img[::-1] return img class DepthBaseDecodeHead(nn.Module): """Base class for BaseDecodeHead. Args: in_channels (List): Input channels. channels (int): Channels after modules, before conv_depth. conv_layer (nn.Module): Conv layers. Default: None. act_layer (nn.Module): Activation layers. Default: nn.ReLU. loss_decode (dict): Config of decode loss. Default: (). sampler (dict|None): The config of depth map sampler. Default: None. align_corners (bool): align_corners argument of F.interpolate. Default: False. min_depth (int): Min depth in dataset setting. Default: 1e-3. max_depth (int): Max depth in dataset setting. Default: None. norm_layer (dict|None): Norm layers. Default: None. classify (bool): Whether predict depth in a cls.-reg. manner. Default: False. n_bins (int): The number of bins used in cls. step. Default: 256. bins_strategy (str): The discrete strategy used in cls. step. Default: 'UD'. norm_strategy (str): The norm strategy on cls. probability distribution. Default: 'linear' scale_up (str): Whether predict depth in a scale-up manner. Default: False. """ def __init__( self, in_channels, conv_layer=None, act_layer=nn.ReLU, channels=96, loss_decode=(), sampler=None, align_corners=False, min_depth=1e-3, max_depth=None, norm_layer=None, classify=False, n_bins=256, bins_strategy="UD", norm_strategy="linear", scale_up=False, ): super(DepthBaseDecodeHead, self).__init__() self.in_channels = in_channels self.channels = channels self.conf_layer = conv_layer self.act_layer = act_layer self.loss_decode = loss_decode self.align_corners = align_corners self.min_depth = min_depth self.max_depth = max_depth self.norm_layer = norm_layer self.classify = classify self.n_bins = n_bins self.scale_up = scale_up if self.classify: assert bins_strategy in ["UD", "SID"], "Support bins_strategy: UD, SID" assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid" self.bins_strategy = bins_strategy self.norm_strategy = norm_strategy self.softmax = nn.Softmax(dim=1) self.conv_depth = nn.Conv2d(channels, n_bins, kernel_size=3, padding=1, stride=1) else: self.conv_depth = nn.Conv2d(channels, 1, kernel_size=3, padding=1, stride=1) self.relu = nn.ReLU() self.sigmoid = nn.Sigmoid() def forward(self, inputs, img_metas): """Placeholder of forward function.""" pass def forward_train(self, img, inputs, img_metas, depth_gt): """Forward function for training. Args: inputs (list[Tensor]): List of multi-level img features. img_metas (list[dict]): List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `depth/datasets/pipelines/formatting.py:Collect`. depth_gt (Tensor): GT depth Returns: dict[str, Tensor]: a dictionary of loss components """ depth_pred = self.forward(inputs, img_metas) losses = self.losses(depth_pred, depth_gt) log_imgs = self.log_images(img[0], depth_pred[0], depth_gt[0], img_metas[0]) losses.update(**log_imgs) return losses def forward_test(self, inputs, img_metas): """Forward function for testing. Args: inputs (list[Tensor]): List of multi-level img features. img_metas (list[dict]): List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `depth/datasets/pipelines/formatting.py:Collect`. Returns: Tensor: Output depth map. """ return self.forward(inputs, img_metas) def depth_pred(self, feat): """Prediction each pixel.""" if self.classify: logit = self.conv_depth(feat) if self.bins_strategy == "UD": bins = torch.linspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device) elif self.bins_strategy == "SID": bins = torch.logspace(self.min_depth, self.max_depth, self.n_bins, device=feat.device) # following Adabins, default linear if self.norm_strategy == "linear": logit = torch.relu(logit) eps = 0.1 logit = logit + eps logit = logit / logit.sum(dim=1, keepdim=True) elif self.norm_strategy == "softmax": logit = torch.softmax(logit, dim=1) elif self.norm_strategy == "sigmoid": logit = torch.sigmoid(logit) logit = logit / logit.sum(dim=1, keepdim=True) output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1) else: if self.scale_up: output = self.sigmoid(self.conv_depth(feat)) * self.max_depth else: output = self.relu(self.conv_depth(feat)) + self.min_depth return output def losses(self, depth_pred, depth_gt): """Compute depth loss.""" loss = dict() depth_pred = resize( input=depth_pred, size=depth_gt.shape[2:], mode="bilinear", align_corners=self.align_corners, warning=False ) if not isinstance(self.loss_decode, nn.ModuleList): losses_decode = [self.loss_decode] else: losses_decode = self.loss_decode for loss_decode in losses_decode: if loss_decode.loss_name not in loss: loss[loss_decode.loss_name] = loss_decode(depth_pred, depth_gt) else: loss[loss_decode.loss_name] += loss_decode(depth_pred, depth_gt) return loss def log_images(self, img_path, depth_pred, depth_gt, img_meta): import numpy as np show_img = copy.deepcopy(img_path.detach().cpu().permute(1, 2, 0)) show_img = show_img.numpy().astype(np.float32) show_img = _imdenormalize( show_img, img_meta["img_norm_cfg"]["mean"], img_meta["img_norm_cfg"]["std"], img_meta["img_norm_cfg"]["to_rgb"], ) show_img = np.clip(show_img, 0, 255) show_img = show_img.astype(np.uint8) show_img = show_img[:, :, ::-1] show_img = show_img.transpose(0, 2, 1) show_img = show_img.transpose(1, 0, 2) depth_pred = depth_pred / torch.max(depth_pred) depth_gt = depth_gt / torch.max(depth_gt) depth_pred_color = copy.deepcopy(depth_pred.detach().cpu()) depth_gt_color = copy.deepcopy(depth_gt.detach().cpu()) return {"img_rgb": show_img, "img_depth_pred": depth_pred_color, "img_depth_gt": depth_gt_color} class BNHead(DepthBaseDecodeHead): """Just a batchnorm.""" def __init__(self, input_transform="resize_concat", in_index=(0, 1, 2, 3), upsample=1, **kwargs): super().__init__(**kwargs) self.input_transform = input_transform self.in_index = in_index self.upsample = upsample # self.bn = nn.SyncBatchNorm(self.in_channels) if self.classify: self.conv_depth = nn.Conv2d(self.channels, self.n_bins, kernel_size=1, padding=0, stride=1) else: self.conv_depth = nn.Conv2d(self.channels, 1, kernel_size=1, padding=0, stride=1) def _transform_inputs(self, inputs): """Transform inputs for decoder. Args: inputs (list[Tensor]): List of multi-level img features. Returns: Tensor: The transformed inputs """ if "concat" in self.input_transform: inputs = [inputs[i] for i in self.in_index] if "resize" in self.input_transform: inputs = [ resize( input=x, size=[s * self.upsample for s in inputs[0].shape[2:]], mode="bilinear", align_corners=self.align_corners, ) for x in inputs ] inputs = torch.cat(inputs, dim=1) elif self.input_transform == "multiple_select": inputs = [inputs[i] for i in self.in_index] else: inputs = inputs[self.in_index] return inputs def _forward_feature(self, inputs, img_metas=None, **kwargs): """Forward function for feature maps before classifying each pixel with ``self.cls_seg`` fc. Args: inputs (list[Tensor]): List of multi-level img features. Returns: feats (Tensor): A tensor of shape (batch_size, self.channels, H, W) which is feature map for last layer of decoder head. """ # accept lists (for cls token) inputs = list(inputs) for i, x in enumerate(inputs): if len(x) == 2: x, cls_token = x[0], x[1] if len(x.shape) == 2: x = x[:, :, None, None] cls_token = cls_token[:, :, None, None].expand_as(x) inputs[i] = torch.cat((x, cls_token), 1) else: x = x[0] if len(x.shape) == 2: x = x[:, :, None, None] inputs[i] = x x = self._transform_inputs(inputs) # feats = self.bn(x) return x def forward(self, inputs, img_metas=None, **kwargs): """Forward function.""" output = self._forward_feature(inputs, img_metas=img_metas, **kwargs) output = self.depth_pred(output) return output class ConvModule(nn.Module): """A conv block that bundles conv/norm/activation layers. This block simplifies the usage of convolution layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU). It is based upon three build methods: `build_conv_layer()`, `build_norm_layer()` and `build_activation_layer()`. Besides, we add some additional features in this module. 1. Automatically set `bias` of the conv layer. 2. Spectral norm is supported. 3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only supports zero and circular padding, and we add "reflect" padding mode. Args: in_channels (int): Number of channels in the input feature map. Same as that in ``nn._ConvNd``. out_channels (int): Number of channels produced by the convolution. Same as that in ``nn._ConvNd``. kernel_size (int | tuple[int]): Size of the convolving kernel. Same as that in ``nn._ConvNd``. stride (int | tuple[int]): Stride of the convolution. Same as that in ``nn._ConvNd``. padding (int | tuple[int]): Zero-padding added to both sides of the input. Same as that in ``nn._ConvNd``. dilation (int | tuple[int]): Spacing between kernel elements. Same as that in ``nn._ConvNd``. groups (int): Number of blocked connections from input channels to output channels. Same as that in ``nn._ConvNd``. bias (bool | str): If specified as `auto`, it will be decided by the norm_layer. Bias will be set as True if `norm_layer` is None, otherwise False. Default: "auto". conv_layer (nn.Module): Convolution layer. Default: None, which means using conv2d. norm_layer (nn.Module): Normalization layer. Default: None. act_layer (nn.Module): Activation layer. Default: nn.ReLU. inplace (bool): Whether to use inplace mode for activation. Default: True. with_spectral_norm (bool): Whether use spectral norm in conv module. Default: False. padding_mode (str): If the `padding_mode` has not been supported by current `Conv2d` in PyTorch, we will use our own padding layer instead. Currently, we support ['zeros', 'circular'] with official implementation and ['reflect'] with our own implementation. Default: 'zeros'. order (tuple[str]): The order of conv/norm/activation layers. It is a sequence of "conv", "norm" and "act". Common examples are ("conv", "norm", "act") and ("act", "conv", "norm"). Default: ('conv', 'norm', 'act'). """ _abbr_ = "conv_block" def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias="auto", conv_layer=nn.Conv2d, norm_layer=None, act_layer=nn.ReLU, inplace=True, with_spectral_norm=False, padding_mode="zeros", order=("conv", "norm", "act"), ): super(ConvModule, self).__init__() official_padding_mode = ["zeros", "circular"] self.conv_layer = conv_layer self.norm_layer = norm_layer self.act_layer = act_layer self.inplace = inplace self.with_spectral_norm = with_spectral_norm self.with_explicit_padding = padding_mode not in official_padding_mode self.order = order assert isinstance(self.order, tuple) and len(self.order) == 3 assert set(order) == set(["conv", "norm", "act"]) self.with_norm = norm_layer is not None self.with_activation = act_layer is not None # if the conv layer is before a norm layer, bias is unnecessary. if bias == "auto": bias = not self.with_norm self.with_bias = bias if self.with_explicit_padding: if padding_mode == "zeros": padding_layer = nn.ZeroPad2d else: raise AssertionError(f"Unsupported padding mode: {padding_mode}") self.pad = padding_layer(padding) # reset padding to 0 for conv module conv_padding = 0 if self.with_explicit_padding else padding # build convolution layer self.conv = self.conv_layer( in_channels, out_channels, kernel_size, stride=stride, padding=conv_padding, dilation=dilation, groups=groups, bias=bias, ) # export the attributes of self.conv to a higher level for convenience self.in_channels = self.conv.in_channels self.out_channels = self.conv.out_channels self.kernel_size = self.conv.kernel_size self.stride = self.conv.stride self.padding = padding self.dilation = self.conv.dilation self.transposed = self.conv.transposed self.output_padding = self.conv.output_padding self.groups = self.conv.groups if self.with_spectral_norm: self.conv = nn.utils.spectral_norm(self.conv) # build normalization layers if self.with_norm: # norm layer is after conv layer if order.index("norm") > order.index("conv"): norm_channels = out_channels else: norm_channels = in_channels norm = partial(norm_layer, num_features=norm_channels) self.add_module("norm", norm) if self.with_bias: from torch.nnModules.batchnorm import _BatchNorm from torch.nnModules.instancenorm import _InstanceNorm if isinstance(norm, (_BatchNorm, _InstanceNorm)): warnings.warn("Unnecessary conv bias before batch/instance norm") else: self.norm_name = None # build activation layer if self.with_activation: # nn.Tanh has no 'inplace' argument # (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.HSigmoid, nn.Swish, nn.GELU) if not isinstance(act_layer, (nn.Tanh, nn.PReLU, nn.Sigmoid, nn.GELU)): act_layer = partial(act_layer, inplace=inplace) self.activate = act_layer() # Use msra init by default self.init_weights() @property def norm(self): if self.norm_name: return getattr(self, self.norm_name) else: return None def init_weights(self): # 1. It is mainly for customized conv layers with their own # initialization manners by calling their own ``init_weights()``, # and we do not want ConvModule to override the initialization. # 2. For customized conv layers without their own initialization # manners (that is, they don't have their own ``init_weights()``) # and PyTorch's conv layers, they will be initialized by # this method with default ``kaiming_init``. # Note: For PyTorch's conv layers, they will be overwritten by our # initialization implementation using default ``kaiming_init``. if not hasattr(self.conv, "init_weights"): if self.with_activation and isinstance(self.act_layer, nn.LeakyReLU): nonlinearity = "leaky_relu" a = 0.01 # XXX: default negative_slope else: nonlinearity = "relu" a = 0 if hasattr(self.conv, "weight") and self.conv.weight is not None: nn.init.kaiming_normal_(self.conv.weight, a=a, mode="fan_out", nonlinearity=nonlinearity) if hasattr(self.conv, "bias") and self.conv.bias is not None: nn.init.constant_(self.conv.bias, 0) if self.with_norm: if hasattr(self.norm, "weight") and self.norm.weight is not None: nn.init.constant_(self.norm.weight, 1) if hasattr(self.norm, "bias") and self.norm.bias is not None: nn.init.constant_(self.norm.bias, 0) def forward(self, x, activate=True, norm=True): for layer in self.order: if layer == "conv": if self.with_explicit_padding: x = self.pad(x) x = self.conv(x) elif layer == "norm" and norm and self.with_norm: x = self.norm(x) elif layer == "act" and activate and self.with_activation: x = self.activate(x) return x class Interpolate(nn.Module): def __init__(self, scale_factor, mode, align_corners=False): super(Interpolate, self).__init__() self.interp = nn.functional.interpolate self.scale_factor = scale_factor self.mode = mode self.align_corners = align_corners def forward(self, x): x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners) return x class HeadDepth(nn.Module): def __init__(self, features): super(HeadDepth, self).__init__() self.head = nn.Sequential( nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), Interpolate(scale_factor=2, mode="bilinear", align_corners=True), nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), ) def forward(self, x): x = self.head(x) return x class ReassembleBlocks(nn.Module): """ViTPostProcessBlock, process cls_token in ViT backbone output and rearrange the feature vector to feature map. Args: in_channels (int): ViT feature channels. Default: 768. out_channels (List): output channels of each stage. Default: [96, 192, 384, 768]. readout_type (str): Type of readout operation. Default: 'ignore'. patch_size (int): The patch size. Default: 16. """ def __init__(self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16): super(ReassembleBlocks, self).__init__() assert readout_type in ["ignore", "add", "project"] self.readout_type = readout_type self.patch_size = patch_size self.projects = nn.ModuleList( [ ConvModule( in_channels=in_channels, out_channels=out_channel, kernel_size=1, act_layer=None, ) for out_channel in out_channels ] ) self.resize_layers = nn.ModuleList( [ nn.ConvTranspose2d( in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0 ), nn.ConvTranspose2d( in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0 ), nn.Identity(), nn.Conv2d( in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1 ), ] ) if self.readout_type == "project": self.readout_projects = nn.ModuleList() for _ in range(len(self.projects)): self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU())) def forward(self, inputs): assert isinstance(inputs, list) out = [] for i, x in enumerate(inputs): assert len(x) == 2 x, cls_token = x[0], x[1] feature_shape = x.shape if self.readout_type == "project": x = x.flatten(2).permute((0, 2, 1)) readout = cls_token.unsqueeze(1).expand_as(x) x = self.readout_projects[i](torch.cat((x, readout), -1)) x = x.permute(0, 2, 1).reshape(feature_shape) elif self.readout_type == "add": x = x.flatten(2) + cls_token.unsqueeze(-1) x = x.reshape(feature_shape) else: pass x = self.projects[i](x) x = self.resize_layers[i](x) out.append(x) return out class PreActResidualConvUnit(nn.Module): """ResidualConvUnit, pre-activate residual unit. Args: in_channels (int): number of channels in the input feature map. act_layer (nn.Module): activation layer. norm_layer (nn.Module): norm layer. stride (int): stride of the first block. Default: 1 dilation (int): dilation rate for convs layers. Default: 1. """ def __init__(self, in_channels, act_layer, norm_layer, stride=1, dilation=1): super(PreActResidualConvUnit, self).__init__() self.conv1 = ConvModule( in_channels, in_channels, 3, stride=stride, padding=dilation, dilation=dilation, norm_layer=norm_layer, act_layer=act_layer, bias=False, order=("act", "conv", "norm"), ) self.conv2 = ConvModule( in_channels, in_channels, 3, padding=1, norm_layer=norm_layer, act_layer=act_layer, bias=False, order=("act", "conv", "norm"), ) def forward(self, inputs): inputs_ = inputs.clone() x = self.conv1(inputs) x = self.conv2(x) return x + inputs_ class FeatureFusionBlock(nn.Module): """FeatureFusionBlock, merge feature map from different stages. Args: in_channels (int): Input channels. act_layer (nn.Module): activation layer for ResidualConvUnit. norm_layer (nn.Module): normalization layer. expand (bool): Whether expand the channels in post process block. Default: False. align_corners (bool): align_corner setting for bilinear upsample. Default: True. """ def __init__(self, in_channels, act_layer, norm_layer, expand=False, align_corners=True): super(FeatureFusionBlock, self).__init__() self.in_channels = in_channels self.expand = expand self.align_corners = align_corners self.out_channels = in_channels if self.expand: self.out_channels = in_channels // 2 self.project = ConvModule(self.in_channels, self.out_channels, kernel_size=1, act_layer=None, bias=True) self.res_conv_unit1 = PreActResidualConvUnit( in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer ) self.res_conv_unit2 = PreActResidualConvUnit( in_channels=self.in_channels, act_layer=act_layer, norm_layer=norm_layer ) def forward(self, *inputs): x = inputs[0] if len(inputs) == 2: if x.shape != inputs[1].shape: res = resize(inputs[1], size=(x.shape[2], x.shape[3]), mode="bilinear", align_corners=False) else: res = inputs[1] x = x + self.res_conv_unit1(res) x = self.res_conv_unit2(x) x = resize(x, scale_factor=2, mode="bilinear", align_corners=self.align_corners) x = self.project(x) return x class DPTHead(DepthBaseDecodeHead): """Vision Transformers for Dense Prediction. This head is implemented of `DPT `_. Args: embed_dims (int): The embed dimension of the ViT backbone. Default: 768. post_process_channels (List): Out channels of post process conv layers. Default: [96, 192, 384, 768]. readout_type (str): Type of readout operation. Default: 'ignore'. patch_size (int): The patch size. Default: 16. expand_channels (bool): Whether expand the channels in post process block. Default: False. """ def __init__( self, embed_dims=768, post_process_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16, expand_channels=False, **kwargs, ): super(DPTHead, self).__init__(**kwargs) self.in_channels = self.in_channels self.expand_channels = expand_channels self.reassemble_blocks = ReassembleBlocks(embed_dims, post_process_channels, readout_type, patch_size) self.post_process_channels = [ channel * math.pow(2, i) if expand_channels else channel for i, channel in enumerate(post_process_channels) ] self.convs = nn.ModuleList() for channel in self.post_process_channels: self.convs.append(ConvModule(channel, self.channels, kernel_size=3, padding=1, act_layer=None, bias=False)) self.fusion_blocks = nn.ModuleList() for _ in range(len(self.convs)): self.fusion_blocks.append(FeatureFusionBlock(self.channels, self.act_layer, self.norm_layer)) self.fusion_blocks[0].res_conv_unit1 = None self.project = ConvModule(self.channels, self.channels, kernel_size=3, padding=1, norm_layer=self.norm_layer) self.num_fusion_blocks = len(self.fusion_blocks) self.num_reassemble_blocks = len(self.reassemble_blocks.resize_layers) self.num_post_process_channels = len(self.post_process_channels) assert self.num_fusion_blocks == self.num_reassemble_blocks assert self.num_reassemble_blocks == self.num_post_process_channels self.conv_depth = HeadDepth(self.channels) def forward(self, inputs, img_metas): assert len(inputs) == self.num_reassemble_blocks x = [inp for inp in inputs] x = self.reassemble_blocks(x) x = [self.convs[i](feature) for i, feature in enumerate(x)] out = self.fusion_blocks[0](x[-1]) for i in range(1, len(self.fusion_blocks)): out = self.fusion_blocks[i](out, x[-(i + 1)]) out = self.project(out) out = self.depth_pred(out) return out ================================================ FILE: dinov2/hub/depth/encoder_decoder.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from collections import OrderedDict import torch import torch.nn as nn import torch.nn.functional as F from .ops import resize def add_prefix(inputs, prefix): """Add prefix for dict. Args: inputs (dict): The input dict with str keys. prefix (str): The prefix to add. Returns: dict: The dict with keys updated with ``prefix``. """ outputs = dict() for name, value in inputs.items(): outputs[f"{prefix}.{name}"] = value return outputs class DepthEncoderDecoder(nn.Module): """Encoder Decoder depther. EncoderDecoder typically consists of backbone and decode_head. """ def __init__(self, backbone, decode_head): super(DepthEncoderDecoder, self).__init__() self.backbone = backbone self.decode_head = decode_head self.align_corners = self.decode_head.align_corners def extract_feat(self, img): """Extract features from images.""" return self.backbone(img) def encode_decode(self, img, img_metas, rescale=True, size=None): """Encode images with backbone and decode into a depth estimation map of the same size as input.""" x = self.extract_feat(img) out = self._decode_head_forward_test(x, img_metas) # crop the pred depth to the certain range. out = torch.clamp(out, min=self.decode_head.min_depth, max=self.decode_head.max_depth) if rescale: if size is None: if img_metas is not None: size = img_metas[0]["ori_shape"][:2] else: size = img.shape[2:] out = resize(input=out, size=size, mode="bilinear", align_corners=self.align_corners) return out def _decode_head_forward_train(self, img, x, img_metas, depth_gt, **kwargs): """Run forward function and calculate loss for decode head in training.""" losses = dict() loss_decode = self.decode_head.forward_train(img, x, img_metas, depth_gt, **kwargs) losses.update(add_prefix(loss_decode, "decode")) return losses def _decode_head_forward_test(self, x, img_metas): """Run forward function and calculate loss for decode head in inference.""" depth_pred = self.decode_head.forward_test(x, img_metas) return depth_pred def forward_dummy(self, img): """Dummy forward function.""" depth = self.encode_decode(img, None) return depth def forward_train(self, img, img_metas, depth_gt, **kwargs): """Forward function for training. Args: img (Tensor): Input images. img_metas (list[dict]): List of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `depth/datasets/pipelines/formatting.py:Collect`. depth_gt (Tensor): Depth gt used if the architecture supports depth estimation task. Returns: dict[str, Tensor]: a dictionary of loss components """ x = self.extract_feat(img) losses = dict() # the last of x saves the info from neck loss_decode = self._decode_head_forward_train(img, x, img_metas, depth_gt, **kwargs) losses.update(loss_decode) return losses def whole_inference(self, img, img_meta, rescale, size=None): """Inference with full image.""" return self.encode_decode(img, img_meta, rescale, size=size) def slide_inference(self, img, img_meta, rescale, stride, crop_size): """Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding. """ h_stride, w_stride = stride h_crop, w_crop = crop_size batch_size, _, h_img, w_img = img.size() h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1 w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1 preds = img.new_zeros((batch_size, 1, h_img, w_img)) count_mat = img.new_zeros((batch_size, 1, h_img, w_img)) for h_idx in range(h_grids): for w_idx in range(w_grids): y1 = h_idx * h_stride x1 = w_idx * w_stride y2 = min(y1 + h_crop, h_img) x2 = min(x1 + w_crop, w_img) y1 = max(y2 - h_crop, 0) x1 = max(x2 - w_crop, 0) crop_img = img[:, :, y1:y2, x1:x2] depth_pred = self.encode_decode(crop_img, img_meta, rescale) preds += F.pad(depth_pred, (int(x1), int(preds.shape[3] - x2), int(y1), int(preds.shape[2] - y2))) count_mat[:, :, y1:y2, x1:x2] += 1 assert (count_mat == 0).sum() == 0 if torch.onnx.is_in_onnx_export(): # cast count_mat to constant while exporting to ONNX count_mat = torch.from_numpy(count_mat.cpu().detach().numpy()).to(device=img.device) preds = preds / count_mat return preds def inference(self, img, img_meta, rescale, size=None, mode="whole"): """Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image info dict where each dict has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys see `depth/datasets/pipelines/formatting.py:Collect`. rescale (bool): Whether rescale back to original shape. Returns: Tensor: The output depth map. """ assert mode in ["slide", "whole"] ori_shape = img_meta[0]["ori_shape"] assert all(_["ori_shape"] == ori_shape for _ in img_meta) if mode == "slide": depth_pred = self.slide_inference(img, img_meta, rescale) else: depth_pred = self.whole_inference(img, img_meta, rescale, size=size) output = depth_pred flip = img_meta[0]["flip"] if flip: flip_direction = img_meta[0]["flip_direction"] assert flip_direction in ["horizontal", "vertical"] if flip_direction == "horizontal": output = output.flip(dims=(3,)) elif flip_direction == "vertical": output = output.flip(dims=(2,)) return output def simple_test(self, img, img_meta, rescale=True): """Simple test with single image.""" depth_pred = self.inference(img, img_meta, rescale) if torch.onnx.is_in_onnx_export(): # our inference backend only support 4D output depth_pred = depth_pred.unsqueeze(0) return depth_pred depth_pred = depth_pred.cpu().numpy() # unravel batch dim depth_pred = list(depth_pred) return depth_pred def aug_test(self, imgs, img_metas, rescale=True): """Test with augmentations. Only rescale=True is supported. """ # aug_test rescale all imgs back to ori_shape for now assert rescale # to save memory, we get augmented depth logit inplace depth_pred = self.inference(imgs[0], img_metas[0], rescale) for i in range(1, len(imgs)): cur_depth_pred = self.inference(imgs[i], img_metas[i], rescale, size=depth_pred.shape[-2:]) depth_pred += cur_depth_pred depth_pred /= len(imgs) depth_pred = depth_pred.cpu().numpy() # unravel batch dim depth_pred = list(depth_pred) return depth_pred def forward_test(self, imgs, img_metas, **kwargs): """ Args: imgs (List[Tensor]): the outer list indicates test-time augmentations and inner Tensor should have a shape NxCxHxW, which contains all images in the batch. img_metas (List[List[dict]]): the outer list indicates test-time augs (multiscale, flip, etc.) and the inner list indicates images in a batch. """ for var, name in [(imgs, "imgs"), (img_metas, "img_metas")]: if not isinstance(var, list): raise TypeError(f"{name} must be a list, but got " f"{type(var)}") num_augs = len(imgs) if num_augs != len(img_metas): raise ValueError(f"num of augmentations ({len(imgs)}) != " f"num of image meta ({len(img_metas)})") # all images in the same aug batch all of the same ori_shape and pad # shape for img_meta in img_metas: ori_shapes = [_["ori_shape"] for _ in img_meta] assert all(shape == ori_shapes[0] for shape in ori_shapes) img_shapes = [_["img_shape"] for _ in img_meta] assert all(shape == img_shapes[0] for shape in img_shapes) pad_shapes = [_["pad_shape"] for _ in img_meta] assert all(shape == pad_shapes[0] for shape in pad_shapes) if num_augs == 1: return self.simple_test(imgs[0], img_metas[0], **kwargs) else: return self.aug_test(imgs, img_metas, **kwargs) def forward(self, img, img_metas, return_loss=True, **kwargs): """Calls either :func:`forward_train` or :func:`forward_test` depending on whether ``return_loss`` is ``True``. Note this setting will change the expected inputs. When ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor and List[dict]), and when ``resturn_loss=False``, img and img_meta should be double nested (i.e. List[Tensor], List[List[dict]]), with the outer list indicating test time augmentations. """ if return_loss: return self.forward_train(img, img_metas, **kwargs) else: return self.forward_test(img, img_metas, **kwargs) def train_step(self, data_batch, optimizer, **kwargs): """The iteration step during training. This method defines an iteration step during training, except for the back propagation and optimizer updating, which are done in an optimizer hook. Note that in some complicated cases or models, the whole process including back propagation and optimizer updating is also defined in this method, such as GAN. Args: data (dict): The output of dataloader. optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of runner is passed to ``train_step()``. This argument is unused and reserved. Returns: dict: It should contain at least 3 keys: ``loss``, ``log_vars``, ``num_samples``. ``loss`` is a tensor for back propagation, which can be a weighted sum of multiple losses. ``log_vars`` contains all the variables to be sent to the logger. ``num_samples`` indicates the batch size (when the model is DDP, it means the batch size on each GPU), which is used for averaging the logs. """ losses = self(**data_batch) # split losses and images real_losses = {} log_imgs = {} for k, v in losses.items(): if "img" in k: log_imgs[k] = v else: real_losses[k] = v loss, log_vars = self._parse_losses(real_losses) outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data_batch["img_metas"]), log_imgs=log_imgs) return outputs def val_step(self, data_batch, **kwargs): """The iteration step during validation. This method shares the same signature as :func:`train_step`, but used during val epochs. Note that the evaluation after training epochs is not implemented with this method, but an evaluation hook. """ output = self(**data_batch, **kwargs) return output @staticmethod def _parse_losses(losses): import torch.distributed as dist """Parse the raw outputs (losses) of the network. Args: losses (dict): Raw output of the network, which usually contain losses and other necessary information. Returns: tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor which may be a weighted sum of all losses, log_vars contains all the variables to be sent to the logger. """ log_vars = OrderedDict() for loss_name, loss_value in losses.items(): if isinstance(loss_value, torch.Tensor): log_vars[loss_name] = loss_value.mean() elif isinstance(loss_value, list): log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) else: raise TypeError(f"{loss_name} is not a tensor or list of tensors") loss = sum(_value for _key, _value in log_vars.items() if "loss" in _key) log_vars["loss"] = loss for loss_name, loss_value in log_vars.items(): # reduce loss when distributed training if dist.is_available() and dist.is_initialized(): loss_value = loss_value.data.clone() dist.all_reduce(loss_value.div_(dist.get_world_size())) log_vars[loss_name] = loss_value.item() return loss, log_vars ================================================ FILE: dinov2/hub/depth/ops.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import warnings import torch.nn.functional as F def resize(input, size=None, scale_factor=None, mode="nearest", align_corners=None, warning=False): if warning: if size is not None and align_corners: input_h, input_w = tuple(int(x) for x in input.shape[2:]) output_h, output_w = tuple(int(x) for x in size) if output_h > input_h or output_w > output_h: if ( (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1) and (output_h - 1) % (input_h - 1) and (output_w - 1) % (input_w - 1) ): warnings.warn( f"When align_corners={align_corners}, " "the output would more aligned if " f"input size {(input_h, input_w)} is `x+1` and " f"out size {(output_h, output_w)} is `nx+1`" ) return F.interpolate(input, size, scale_factor, mode, align_corners) ================================================ FILE: dinov2/hub/depthers.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from enum import Enum from functools import partial from typing import Optional, Tuple, Union import torch from .backbones import _make_dinov2_model from .depth import BNHead, DepthEncoderDecoder, DPTHead from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name, CenterPadding class Weights(Enum): NYU = "NYU" KITTI = "KITTI" def _get_depth_range(pretrained: bool, weights: Weights = Weights.NYU) -> Tuple[float, float]: if not pretrained: # Default return (0.001, 10.0) # Pretrained, set according to the training dataset for the provided weights if weights == Weights.KITTI: return (0.001, 80.0) if weights == Weights.NYU: return (0.001, 10.0) return (0.001, 10.0) def _make_dinov2_linear_depth_head( *, embed_dim: int, layers: int, min_depth: float, max_depth: float, **kwargs, ): if layers not in (1, 4): raise AssertionError(f"Unsupported number of layers: {layers}") if layers == 1: in_index = [0] else: assert layers == 4 in_index = [0, 1, 2, 3] return BNHead( classify=True, n_bins=256, bins_strategy="UD", norm_strategy="linear", upsample=4, in_channels=[embed_dim] * len(in_index), in_index=in_index, input_transform="resize_concat", channels=embed_dim * len(in_index) * 2, align_corners=False, min_depth=0.001, max_depth=80, loss_decode=(), ) def _make_dinov2_linear_depther( *, arch_name: str = "vit_large", layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, depth_range: Optional[Tuple[float, float]] = None, **kwargs, ): if layers not in (1, 4): raise AssertionError(f"Unsupported number of layers: {layers}") if isinstance(weights, str): try: weights = Weights[weights] except KeyError: raise AssertionError(f"Unsupported weights: {weights}") if depth_range is None: depth_range = _get_depth_range(pretrained, weights) min_depth, max_depth = depth_range backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs) embed_dim = backbone.embed_dim patch_size = backbone.patch_size model_name = _make_dinov2_model_name(arch_name, patch_size) linear_depth_head = _make_dinov2_linear_depth_head( embed_dim=embed_dim, layers=layers, min_depth=min_depth, max_depth=max_depth, ) layer_count = { "vit_small": 12, "vit_base": 12, "vit_large": 24, "vit_giant2": 40, }[arch_name] if layers == 4: out_index = { "vit_small": [2, 5, 8, 11], "vit_base": [2, 5, 8, 11], "vit_large": [4, 11, 17, 23], "vit_giant2": [9, 19, 29, 39], }[arch_name] else: assert layers == 1 out_index = [layer_count - 1] model = DepthEncoderDecoder(backbone=backbone, decode_head=linear_depth_head) model.backbone.forward = partial( backbone.get_intermediate_layers, n=out_index, reshape=True, return_class_token=True, norm=False, ) model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(patch_size)(x[0])) if pretrained: layers_str = str(layers) if layers == 4 else "" weights_str = weights.value.lower() url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_linear{layers_str}_head.pth" checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu") if "state_dict" in checkpoint: state_dict = checkpoint["state_dict"] model.load_state_dict(state_dict, strict=False) return model def dinov2_vits14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs): return _make_dinov2_linear_depther( arch_name="vit_small", layers=layers, pretrained=pretrained, weights=weights, **kwargs ) def dinov2_vitb14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs): return _make_dinov2_linear_depther( arch_name="vit_base", layers=layers, pretrained=pretrained, weights=weights, **kwargs ) def dinov2_vitl14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs): return _make_dinov2_linear_depther( arch_name="vit_large", layers=layers, pretrained=pretrained, weights=weights, **kwargs ) def dinov2_vitg14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs): return _make_dinov2_linear_depther( arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs ) def _make_dinov2_dpt_depth_head(*, embed_dim: int, min_depth: float, max_depth: float): return DPTHead( in_channels=[embed_dim] * 4, channels=256, embed_dims=embed_dim, post_process_channels=[embed_dim // 2 ** (3 - i) for i in range(4)], readout_type="project", min_depth=min_depth, max_depth=max_depth, loss_decode=(), ) def _make_dinov2_dpt_depther( *, arch_name: str = "vit_large", pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, depth_range: Optional[Tuple[float, float]] = None, **kwargs, ): if isinstance(weights, str): try: weights = Weights[weights] except KeyError: raise AssertionError(f"Unsupported weights: {weights}") if depth_range is None: depth_range = _get_depth_range(pretrained, weights) min_depth, max_depth = depth_range backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs) model_name = _make_dinov2_model_name(arch_name, backbone.patch_size) dpt_depth_head = _make_dinov2_dpt_depth_head(embed_dim=backbone.embed_dim, min_depth=min_depth, max_depth=max_depth) out_index = { "vit_small": [2, 5, 8, 11], "vit_base": [2, 5, 8, 11], "vit_large": [4, 11, 17, 23], "vit_giant2": [9, 19, 29, 39], }[arch_name] model = DepthEncoderDecoder(backbone=backbone, decode_head=dpt_depth_head) model.backbone.forward = partial( backbone.get_intermediate_layers, n=out_index, reshape=True, return_class_token=True, norm=False, ) model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone.patch_size)(x[0])) if pretrained: weights_str = weights.value.lower() url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_dpt_head.pth" checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu") if "state_dict" in checkpoint: state_dict = checkpoint["state_dict"] model.load_state_dict(state_dict, strict=False) return model def dinov2_vits14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs): return _make_dinov2_dpt_depther(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs) def dinov2_vitb14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs): return _make_dinov2_dpt_depther(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs) def dinov2_vitl14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs): return _make_dinov2_dpt_depther(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs) def dinov2_vitg14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs): return _make_dinov2_dpt_depther( arch_name="vit_giant2", ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs ) ================================================ FILE: dinov2/hub/dinotxt.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import math from .backbones import dinov2_vitl14_reg from .utils import _DINOV2_BASE_URL def dinov2_vitl14_reg4_dinotxt_tet1280d20h24l(): from .text.dinotxt_model import DinoTxtConfig, DinoTxt from .text.dinov2_wrapper import DINOv2Wrapper from .text.text_transformer import TextTransformer dinotxt_config = DinoTxtConfig( embed_dim=2048, vision_model_freeze_backbone=True, vision_model_train_img_size=224, vision_model_use_class_token=True, vision_model_use_patch_tokens=True, vision_model_num_head_blocks=2, vision_model_head_blocks_drop_path=0.3, vision_model_use_linear_projection=False, vision_model_patch_tokens_pooler_type="mean", vision_model_patch_token_layer=1, # which layer to take patch tokens from # 1 - last layer, 2 - second last layer, etc. text_model_freeze_backbone=False, text_model_num_head_blocks=0, text_model_head_blocks_is_causal=False, text_model_head_blocks_drop_prob=0.0, text_model_tokens_pooler_type="argmax", text_model_use_linear_projection=True, init_logit_scale=math.log(1 / 0.07), init_logit_bias=None, freeze_logit_scale=False, ) vision_backbone = DINOv2Wrapper(dinov2_vitl14_reg()) text_backbone = TextTransformer( context_length=77, vocab_size=49408, dim=1280, num_heads=20, num_layers=24, ffn_ratio=4, is_causal=True, ls_init_value=None, dropout_prob=0.0, ) model = DinoTxt(dinotxt_config, vision_backbone, text_backbone) model.init_weights() model.visual_model.backbone = vision_backbone model.eval() visual_model_head_state_dict = torch.hub.load_state_dict_from_url( _DINOV2_BASE_URL + "/dinov2_vitl14/dinov2_vitl14_reg4_dinotxt_tet1280d20h24l_vision_head.pth", map_location="cpu", ) text_model_state_dict = torch.hub.load_state_dict_from_url( _DINOV2_BASE_URL + "/dinov2_vitl14/dinov2_vitl14_reg4_dinotxt_tet1280d20h24l_text_encoder.pth", map_location="cpu", ) model.visual_model.head.load_state_dict(visual_model_head_state_dict, strict=True) model.text_model.load_state_dict(text_model_state_dict, strict=True) return model def get_tokenizer(): from .text.tokenizer import Tokenizer import requests from io import BytesIO url = _DINOV2_BASE_URL + "/thirdparty/bpe_simple_vocab_16e6.txt.gz" try: response = requests.get(url) response.raise_for_status() file_buf = BytesIO(response.content) return Tokenizer(vocab_path=file_buf) except Exception as e: raise FileNotFoundError(f"Failed to download file from url {url} with error last: {e}") ================================================ FILE: dinov2/hub/text/dinotxt_model.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import math from dataclasses import dataclass from typing import Optional, Tuple import torch import torch.nn.functional as F from torch import nn, Tensor from .vision_tower import VisionTower from .text_tower import TextTower @dataclass class DinoTxtConfig: embed_dim: int vision_model_freeze_backbone: bool = True vision_model_train_img_size: int = 224 vision_model_use_class_token: bool = True vision_model_use_patch_tokens: bool = False vision_model_num_head_blocks: int = 0 vision_model_head_blocks_drop_path: float = 0.3 vision_model_use_linear_projection: bool = False vision_model_patch_tokens_pooler_type: str = "mean" vision_model_patch_token_layer: int = 1 # which layer to take patch tokens from # 1 - last layer, 2 - second last layer, etc. text_model_freeze_backbone: bool = False text_model_num_head_blocks: int = 0 text_model_head_blocks_is_causal: bool = False text_model_head_blocks_drop_prob: float = 0.0 text_model_tokens_pooler_type: str = "first" text_model_use_linear_projection: bool = False init_logit_scale: float = math.log(1 / 0.07) init_logit_bias: Optional[float] = None freeze_logit_scale: bool = False class DinoTxt(nn.Module): def __init__( self, model_config: DinoTxtConfig, vision_backbone: nn.Module, text_backbone: nn.Module, ): super().__init__() self.model_config = model_config self.visual_model = VisionTower( vision_backbone, model_config.vision_model_freeze_backbone, model_config.embed_dim, model_config.vision_model_num_head_blocks, model_config.vision_model_head_blocks_drop_path, model_config.vision_model_use_class_token, model_config.vision_model_use_patch_tokens, model_config.vision_model_patch_token_layer, model_config.vision_model_patch_tokens_pooler_type, model_config.vision_model_use_linear_projection, ) self.text_model = TextTower( text_backbone, model_config.text_model_freeze_backbone, model_config.embed_dim, model_config.text_model_num_head_blocks, model_config.text_model_head_blocks_is_causal, model_config.text_model_head_blocks_drop_prob, model_config.text_model_tokens_pooler_type, model_config.text_model_use_linear_projection, ) self.logit_scale = nn.Parameter(torch.ones(1) * model_config.init_logit_scale) if model_config.freeze_logit_scale: self.logit_scale.requires_grad = False def init_weights(self): self.visual_model.init_weights() self.text_model.init_weights() def get_visual_class_and_patch_tokens(self, image: Tensor) -> Tuple[Tensor, Tensor]: return self.visual_model.get_class_and_patch_tokens(image) def encode_image( self, image: Tensor, normalize: bool = False, ) -> Tensor: """ Encode an image into a vector descriptor containing both global and local features. Args: image (Tensor): Tensor of shape `(batch_size, rgb, height, width)`, normalized using ImageNet mean and std. normalize (bool, optional): Whether to normalize the output vectors. Default is False. Image features should always be normalized before comparing them with text features: Returns: Tensor: Tensor of shape `(batch_size, embed_dim)` containing the image features. The first half of the vector corresponds to the global features (class token), and the second half corresponds to the pooled patch features. """ features = self.visual_model(image) return F.normalize(features, dim=-1) if normalize else features def encode_text(self, text: Tensor, normalize: bool = False) -> Tensor: """ Encode a text input into a vector descriptor. Args: text (Tensor): Tensor of shape `(batch_size, seq_len)` containing token indices. normalize (bool, optional): Whether to normalize the output vectors. Default is False. Text features should be normalized before comparing them with image features: Returns: Tensor: Tensor of shape `(batch_size, embed_dim)` containing the text features. As a consequence of the training procedure, assume that the first half of the tensor corresponds to global image features and the second half to pooled patch features. """ features = self.text_model(text) return F.normalize(features, dim=-1) if normalize else features def get_logits(self, image: Tensor, text: Tensor) -> Tuple[Tensor, Tensor]: text_features = self.encode_text(text, normalize=True) image_features = self.encode_image(image, normalize=True) image_logits = self.logit_scale.exp() * image_features @ text_features.T text_logits = image_logits.T return image_logits, text_logits def forward( self, image: Tensor, text: Tensor, ) -> Tuple[Tensor, Tensor, Tensor]: text_features = self.encode_text(text, normalize=True) image_features = self.encode_image(image, normalize=True) return image_features, text_features, self.logit_scale.exp() ================================================ FILE: dinov2/hub/text/dinov2_wrapper.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from typing import Sequence import torch class DINOv2Wrapper(torch.nn.Module): def __init__(self, model): super().__init__() self.model = model self.embed_dim = model.embed_dim self.num_heads = model.num_heads self.num_register_tokens = model.num_register_tokens # Same as the original forward, but assert is_training and rename x_norm_regtokens -> x_storage_tokens def forward(self, img, is_training: bool): assert is_training H, W = img.shape[-2:] P = self.model.patch_size x_dict = self.model(img, is_training=True) x_dict["h"] = h = H // P x_dict["w"] = w = W // P assert x_dict["x_norm_patchtokens"].shape[-2] == h * w return x_dict # Same as the original get_intermediate_layers, but allow returining extra tokens (registers) def get_intermediate_layers( self, x: torch.Tensor, n: int | Sequence[int] = 1, # Layers or n last layers to take reshape: bool = False, return_class_token: bool = False, return_register_tokens: bool = False, norm=True, ) -> tuple[torch.Tensor] | tuple[tuple[torch.Tensor, ...], ...]: if self.model.chunked_blocks: outputs = self.model._get_intermediate_layers_chunked(x, n) else: outputs = self.model._get_intermediate_layers_not_chunked(x, n) if norm: outputs = [self.model.norm(out) for out in outputs] class_tokens = [out[:, 0] for out in outputs] register_tokens = [out[:, 1 : 1 + self.model.num_register_tokens] for out in outputs] outputs = [out[:, 1 + self.model.num_register_tokens :] for out in outputs] if reshape: B, _, h, w = x.shape outputs = [ out.reshape(B, h // self.model.patch_size, w // self.model.patch_size, -1) .permute(0, 3, 1, 2) .contiguous() for out in outputs ] if not return_class_token and not return_register_tokens: return tuple(outputs) if return_class_token and not return_register_tokens: return tuple(zip(outputs, class_tokens)) if not return_class_token and return_register_tokens: return tuple(zip(outputs, register_tokens)) return tuple(zip(outputs, class_tokens, register_tokens)) ================================================ FILE: dinov2/hub/text/text_tower.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch from torch import nn, Tensor from dinov2.layers import ( CausalAttentionBlock, ) class TextHead(nn.Module): def __init__( self, input_dim: int, embed_dim: int, num_heads: int, num_blocks: int, block_drop_prob: float, is_causal: bool, use_linear_projection: bool, ): super().__init__() block_list = [nn.Identity()] self.ln_final = nn.Identity() if num_blocks > 0: block_list = [ CausalAttentionBlock( dim=input_dim, num_heads=num_heads, is_causal=is_causal, dropout_prob=block_drop_prob, ) for _ in range(num_blocks) ] self.ln_final = nn.LayerNorm(input_dim) self.block_list = nn.ModuleList(block_list) self.num_blocks = num_blocks self.linear_projection = nn.Identity() if input_dim != embed_dim or use_linear_projection: self.linear_projection = nn.Linear(input_dim, embed_dim, bias=False) def init_weights(self): if self.num_blocks > 0: for i in range(self.num_blocks): self.block_list[i].init_weights() self.ln_final.reset_parameters() if isinstance(self.linear_projection, nn.Linear): nn.init.normal_(self.linear_projection.weight, std=self.linear_projection.in_features**-0.5) def forward(self, text_tokens: Tensor) -> Tensor: for block in self.block_list: text_tokens = block(text_tokens) text_tokens = self.ln_final(text_tokens) return self.linear_projection(text_tokens) class TextTower(nn.Module): def __init__( self, backbone: nn.Module, freeze_backbone: bool, embed_dim: int, num_head_blocks: int, head_blocks_is_causal: bool, head_blocks_block_drop_prob: float, tokens_pooler_type: str, use_linear_projection: bool, ): super().__init__() self.backbone = backbone self.freeze_backbone = freeze_backbone backbone_out_dim = backbone.embed_dim self.backbone = backbone self.head = TextHead( backbone_out_dim, embed_dim, self.backbone.num_heads, num_head_blocks, head_blocks_block_drop_prob, head_blocks_is_causal, use_linear_projection, ) self.tokens_pooler_type = tokens_pooler_type def init_weights(self): self.backbone.init_weights() self.head.init_weights() def forward(self, token_indices: Tensor) -> Tensor: text_tokens = self.backbone(token_indices) text_tokens = self.head(text_tokens) if self.tokens_pooler_type == "first": features = text_tokens[:, 0] elif self.tokens_pooler_type == "last": features = text_tokens[:, -1] elif self.tokens_pooler_type == "argmax": assert token_indices is not None features = text_tokens[torch.arange(text_tokens.shape[0]), token_indices.argmax(dim=-1)] else: raise ValueError(f"Unknown text tokens pooler type: {self.pooler_type}") return features ================================================ FILE: dinov2/hub/text/text_transformer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from typing import Callable, Optional, Tuple import torch from torch import nn, Tensor from dinov2.layers import CausalAttentionBlock class TextTransformer(nn.Module): def __init__( self, context_length: int, vocab_size: int, dim: int, num_heads: int, num_layers: int, ffn_ratio: float, is_causal: bool, ls_init_value: Optional[float] = None, act_layer: Callable = nn.GELU, norm_layer: Callable = nn.LayerNorm, dropout_prob: float = 0.0, ): super().__init__() self.vocab_size = vocab_size self.embed_dim = dim self.num_heads = num_heads self.token_embedding = nn.Embedding(vocab_size, dim) self.positional_embedding = nn.Parameter(torch.empty(context_length, dim)) self.dropout = nn.Dropout(dropout_prob) self.num_layers = num_layers block_list = [ CausalAttentionBlock( dim=dim, num_heads=num_heads, ffn_ratio=ffn_ratio, ls_init_value=ls_init_value, is_causal=is_causal, act_layer=act_layer, norm_layer=norm_layer, dropout_prob=dropout_prob, ) for _ in range(num_layers) ] self.blocks = nn.ModuleList(block_list) self.ln_final = norm_layer(dim) def init_weights(self): nn.init.normal_(self.token_embedding.weight, std=0.02) nn.init.normal_(self.positional_embedding, std=0.01) init_attn_std = self.embed_dim**-0.5 init_proj_std = (self.embed_dim**-0.5) * ((2 * self.num_layers) ** -0.5) init_fc_std = (2 * self.embed_dim) ** -0.5 for block in self.blocks: block.init_weights(init_attn_std, init_proj_std, init_fc_std) self.ln_final.reset_parameters() def forward(self, token_indices: Tensor) -> Tuple[torch.Tensor, torch.Tensor]: _, N = token_indices.size() x = self.token_embedding(token_indices) + self.positional_embedding[:N] x = self.dropout(x) for block in self.blocks: x = block(x) x = self.ln_final(x) return x ================================================ FILE: dinov2/hub/text/tokenizer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch from typing import List, Union from dinov2.thirdparty.CLIP.clip.simple_tokenizer import SimpleTokenizer class Tokenizer(SimpleTokenizer): def __init__(self, vocab_path: str): SimpleTokenizer.__init__(self, bpe_path=vocab_path) def tokenize(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: """ Returns the tokenized representation of given input string(s) Parameters ---------- texts : Union[str, List[str]] An input string or a list of input strings to tokenize context_length : int The context length to use; all CLIP models use 77 as the context length Returns ------- A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] """ if isinstance(texts, str): texts = [texts] sot_token = self.encoder["<|startoftext|>"] eot_token = self.encoder["<|endoftext|>"] all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts] result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) for i, tokens in enumerate(all_tokens): if len(tokens) > context_length: tokens = tokens[:context_length] # Truncate tokens[-1] = eot_token result[i, : len(tokens)] = torch.tensor(tokens) return result ================================================ FILE: dinov2/hub/text/vision_tower.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from functools import partial from typing import Callable, Tuple import torch from torch import nn, Tensor from dinov2.layers import ( LayerScale, NestedTensorBlock as AttentionBlock, SwiGLUFFNAligned as SwiGLUFFN, ) def init_weights_vit_timm(module: nn.Module, name: str = ""): """ViT weight initialization, original timm impl (for reproducibility)""" if isinstance(module, nn.Linear): nn.init.trunc_normal_(module.weight, std=0.02) if module.bias is not None: nn.init.zeros_(module.bias) if isinstance(module, nn.LayerNorm): module.reset_parameters() if isinstance(module, LayerScale): module.reset_parameters() if isinstance(module, nn.Conv2d): module.reset_parameters() def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module: if not depth_first and include_root: fn(module=module, name=name) for child_name, child_module in module.named_children(): child_name = ".".join((name, child_name)) if name else child_name named_apply( fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True, ) if depth_first and include_root: fn(module=module, name=name) return module class VisionHead(nn.Module): def __init__( self, input_dim: int, embed_dim: int, num_heads: int, num_blocks: int, blocks_drop_path: float, use_class_token: bool, use_patch_tokens: bool, use_linear_projection: bool, ): super().__init__() block_list = [nn.Identity()] self.ln_final = nn.Identity() if num_blocks > 0: block_list = [ AttentionBlock( input_dim, num_heads, ffn_layer=partial(SwiGLUFFN, align_to=64), init_values=1e-5, drop_path=blocks_drop_path, ) for _ in range(num_blocks) ] self.ln_final = nn.LayerNorm(input_dim) self.block_list = nn.ModuleList(block_list) self.num_blocks = num_blocks multiplier = 2 if use_class_token and use_patch_tokens else 1 self.linear_projection = nn.Identity() if multiplier * input_dim != embed_dim or use_linear_projection: assert embed_dim % multiplier == 0, f"Expects {embed_dim} to be divisible by {multiplier}" self.linear_projection = nn.Linear(input_dim, embed_dim // multiplier, bias=False) def init_weights(self): if self.num_blocks > 0: for i in range(self.num_blocks): block = self.block_list[i] named_apply(init_weights_vit_timm, block) self.ln_final.reset_parameters() if isinstance(self.linear_projection, nn.Linear): nn.init.normal_(self.linear_projection.weight, std=self.linear_projection.in_features**-0.5) def forward(self, image_tokens: Tensor) -> Tensor: for block in self.block_list: image_tokens = block(image_tokens) image_tokens = self.ln_final(image_tokens) return self.linear_projection(image_tokens) class VisionTower(nn.Module): def __init__( self, backbone: nn.Module, freeze_backbone: bool, embed_dim: int, num_head_blocks: int, head_blocks_block_drop_path: float, use_class_token: bool, use_patch_tokens: bool, patch_token_layer: int, patch_tokens_pooler_type: str, use_linear_projection: bool, ): super().__init__() self.backbone = backbone self.freeze_backbone = freeze_backbone self.use_class_token = use_class_token self.use_patch_tokens = use_patch_tokens self.patch_token_layer = patch_token_layer self.patch_tokens_pooler_type = patch_tokens_pooler_type self.num_register_tokens = 0 if hasattr(self.backbone, "num_register_tokens"): self.num_register_tokens = self.backbone.num_register_tokens elif hasattr(self.backbone, "n_storage_tokens"): self.num_register_tokens = self.backbone.n_storage_tokens backbone_out_dim = self.backbone.embed_dim self.head = VisionHead( backbone_out_dim, embed_dim, self.backbone.num_heads, num_head_blocks, head_blocks_block_drop_path, use_class_token, use_patch_tokens, use_linear_projection, ) def init_weights(self): if not self.freeze_backbone: self.backbone.init_weights() self.head.init_weights() def get_backbone_features(self, images: Tensor) -> Tuple[Tensor, Tensor, Tensor]: tokens = self.backbone.get_intermediate_layers( images, n=self.patch_token_layer, return_class_token=True, return_register_tokens=True, ) class_token = tokens[-1][1] patch_tokens = tokens[0][0] register_tokens = tokens[0][2] return class_token, patch_tokens, register_tokens def get_class_and_patch_tokens(self, images: Tensor) -> Tuple[Tensor, Tensor]: class_token, patch_tokens, register_tokens = self.get_backbone_features(images) image_tokens = self.head(torch.cat([class_token.unsqueeze(1), register_tokens, patch_tokens], dim=1)) class_token, patch_tokens = image_tokens[:, 0], image_tokens[:, self.num_register_tokens + 1 :] return class_token, patch_tokens def forward(self, images: Tensor) -> Tensor: class_token, patch_tokens = self.get_class_and_patch_tokens(images) features = [] if self.use_class_token: features.append(class_token) if self.use_patch_tokens: if self.patch_tokens_pooler_type == "mean": features.append(torch.mean(patch_tokens, dim=1)) elif self.patch_tokens_pooler_type == "max": features.append(torch.max(patch_tokens, dim=1).values) elif self.patch_tokens_pooler_type == "gem": power = 3 eps = 1e-6 patch_tokens_power = patch_tokens.clamp(min=eps).pow(power) features.append(torch.mean(patch_tokens_power, dim=1).pow(1 / power)) else: raise ValueError(f"Unknown patch tokens pooler type: {self.patch_tokens_pooler_type}") return torch.cat(features, dim=-1) ================================================ FILE: dinov2/hub/utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import itertools import math import torch import torch.nn as nn import torch.nn.functional as F _DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2" def _make_dinov2_model_name(arch_name: str, patch_size: int, num_register_tokens: int = 0) -> str: compact_arch_name = arch_name.replace("_", "")[:4] registers_suffix = f"_reg{num_register_tokens}" if num_register_tokens else "" return f"dinov2_{compact_arch_name}{patch_size}{registers_suffix}" class CenterPadding(nn.Module): def __init__(self, multiple): super().__init__() self.multiple = multiple def _get_pad(self, size): new_size = math.ceil(size / self.multiple) * self.multiple pad_size = new_size - size pad_size_left = pad_size // 2 pad_size_right = pad_size - pad_size_left return pad_size_left, pad_size_right @torch.inference_mode() def forward(self, x): pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1])) output = F.pad(x, pads) return output ================================================ FILE: dinov2/hub/xray_dino/backbones.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the licence # found in the LICENSE_XRAY_DINO_MODEL file in the root directory of this source tree. from typing import Union from ..backbones import Weights, _make_dinov2_model def xray_dino_vitl16(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.XRAY_DINO, **kwargs): """ XRay-DINO ViT-L/16 model (optionally) pretrained on the XRay-DINO dataset. """ return _make_dinov2_model( arch_name="vit_large", patch_size=16, img_size=512, num_register_tokens=0, interpolate_antialias=False, interpolate_offset=0.1, block_chunks=4, pretrained=pretrained, weights=weights, hash="ad31c2b0", check_hash=True, **kwargs, ) ================================================ FILE: dinov2/layers/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .dino_head import DINOHead from .layer_scale import LayerScale from .mlp import Mlp from .patch_embed import PatchEmbed from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused, SwiGLUFFNAligned from .block import NestedTensorBlock, CausalAttentionBlock from .attention import Attention, MemEffAttention ================================================ FILE: dinov2/layers/attention.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py # https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py import logging import os import warnings import torch from torch import nn, Tensor logger = logging.getLogger("dinov2") XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None try: if XFORMERS_ENABLED: from xformers.ops import memory_efficient_attention, unbind XFORMERS_AVAILABLE = True warnings.warn("xFormers is available (Attention)") else: warnings.warn("xFormers is disabled (Attention)") raise ImportError except ImportError: XFORMERS_AVAILABLE = False warnings.warn("xFormers is not available (Attention)") class Attention(nn.Module): def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bias: bool = True, attn_drop: float = 0.0, proj_drop: float = 0.0, ) -> None: super().__init__() self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = attn_drop self.proj = nn.Linear(dim, dim, bias=proj_bias) self.proj_drop = nn.Dropout(proj_drop) def init_weights( self, init_attn_std: float | None = None, init_proj_std: float | None = None, factor: float = 1.0 ) -> None: init_attn_std = init_attn_std or (self.dim**-0.5) init_proj_std = init_proj_std or init_attn_std * factor nn.init.normal_(self.qkv.weight, std=init_attn_std) nn.init.normal_(self.proj.weight, std=init_proj_std) if self.qkv.bias is not None: nn.init.zeros_(self.qkv.bias) if self.proj.bias is not None: nn.init.zeros_(self.proj.bias) def forward(self, x: Tensor, is_causal: bool = False) -> Tensor: B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) q, k, v = torch.unbind(qkv, 2) q, k, v = [t.transpose(1, 2) for t in [q, k, v]] x = nn.functional.scaled_dot_product_attention( q, k, v, attn_mask=None, dropout_p=self.attn_drop if self.training else 0, is_causal=is_causal ) x = x.transpose(1, 2).contiguous().view(B, N, C) x = self.proj_drop(self.proj(x)) return x class MemEffAttention(Attention): def forward(self, x: Tensor, attn_bias=None) -> Tensor: if not XFORMERS_AVAILABLE: if attn_bias is not None: raise AssertionError("xFormers is required for using nested tensors") return super().forward(x) B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) q, k, v = unbind(qkv, 2) x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) x = x.reshape([B, N, C]) x = self.proj(x) x = self.proj_drop(x) return x ================================================ FILE: dinov2/layers/block.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py # https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py import logging import os from typing import Callable, List, Any, Tuple, Dict, Optional import warnings import torch from torch import nn, Tensor from .attention import Attention, MemEffAttention from .drop_path import DropPath from .layer_scale import LayerScale from .mlp import Mlp logger = logging.getLogger("dinov2") XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None try: if XFORMERS_ENABLED: from xformers.ops import fmha, scaled_index_add, index_select_cat XFORMERS_AVAILABLE = True warnings.warn("xFormers is available (Block)") else: warnings.warn("xFormers is disabled (Block)") raise ImportError except ImportError: XFORMERS_AVAILABLE = False warnings.warn("xFormers is not available (Block)") class Block(nn.Module): def __init__( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bool = False, proj_bias: bool = True, ffn_bias: bool = True, drop: float = 0.0, attn_drop: float = 0.0, init_values=None, drop_path: float = 0.0, act_layer: Callable[..., nn.Module] = nn.GELU, norm_layer: Callable[..., nn.Module] = nn.LayerNorm, attn_class: Callable[..., nn.Module] = Attention, ffn_layer: Callable[..., nn.Module] = Mlp, ) -> None: super().__init__() # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}") self.norm1 = norm_layer(dim) self.attn = attn_class( dim, num_heads=num_heads, qkv_bias=qkv_bias, proj_bias=proj_bias, attn_drop=attn_drop, proj_drop=drop, ) self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = ffn_layer( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, bias=ffn_bias, ) self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.sample_drop_ratio = drop_path def forward(self, x: Tensor) -> Tensor: def attn_residual_func(x: Tensor) -> Tensor: return self.ls1(self.attn(self.norm1(x))) def ffn_residual_func(x: Tensor) -> Tensor: return self.ls2(self.mlp(self.norm2(x))) if self.training and self.sample_drop_ratio > 0.1: # the overhead is compensated only for a drop path rate larger than 0.1 x = drop_add_residual_stochastic_depth( x, residual_func=attn_residual_func, sample_drop_ratio=self.sample_drop_ratio, ) x = drop_add_residual_stochastic_depth( x, residual_func=ffn_residual_func, sample_drop_ratio=self.sample_drop_ratio, ) elif self.training and self.sample_drop_ratio > 0.0: x = x + self.drop_path1(attn_residual_func(x)) x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2 else: x = x + attn_residual_func(x) x = x + ffn_residual_func(x) return x class CausalAttentionBlock(nn.Module): def __init__( self, dim: int, num_heads: int, ffn_ratio: float = 4.0, ls_init_value: Optional[float] = None, is_causal: bool = True, act_layer: Callable = nn.GELU, norm_layer: Callable = nn.LayerNorm, dropout_prob: float = 0.0, ): super().__init__() self.dim = dim self.is_causal = is_causal self.ls1 = LayerScale(dim, init_values=ls_init_value) if ls_init_value else nn.Identity() self.attention_norm = norm_layer(dim) self.attention = Attention(dim, num_heads, attn_drop=dropout_prob, proj_drop=dropout_prob) self.ffn_norm = norm_layer(dim) ffn_hidden_dim = int(dim * ffn_ratio) self.feed_forward = Mlp( in_features=dim, hidden_features=ffn_hidden_dim, drop=dropout_prob, act_layer=act_layer, ) self.ls2 = LayerScale(dim, init_values=ls_init_value) if ls_init_value else nn.Identity() def init_weights( self, init_attn_std: float | None = None, init_proj_std: float | None = None, init_fc_std: float | None = None, factor: float = 1.0, ) -> None: init_attn_std = init_attn_std or (self.dim**-0.5) init_proj_std = init_proj_std or init_attn_std * factor init_fc_std = init_fc_std or (2 * self.dim) ** -0.5 self.attention.init_weights(init_attn_std, init_proj_std) self.attention_norm.reset_parameters() nn.init.normal_(self.feed_forward.fc1.weight, std=init_fc_std) nn.init.normal_(self.feed_forward.fc2.weight, std=init_proj_std) self.ffn_norm.reset_parameters() def forward( self, x: torch.Tensor, ): x_attn = x + self.ls1(self.attention(self.attention_norm(x), self.is_causal)) x_ffn = x_attn + self.ls2(self.feed_forward(self.ffn_norm(x_attn))) return x_ffn def drop_add_residual_stochastic_depth( x: Tensor, residual_func: Callable[[Tensor], Tensor], sample_drop_ratio: float = 0.0, ) -> Tensor: # 1) extract subset using permutation b, n, d = x.shape sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) brange = (torch.randperm(b, device=x.device))[:sample_subset_size] x_subset = x[brange] # 2) apply residual_func to get residual residual = residual_func(x_subset) x_flat = x.flatten(1) residual = residual.flatten(1) residual_scale_factor = b / sample_subset_size # 3) add the residual x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) return x_plus_residual.view_as(x) def get_branges_scales(x, sample_drop_ratio=0.0): b, n, d = x.shape sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) brange = (torch.randperm(b, device=x.device))[:sample_subset_size] residual_scale_factor = b / sample_subset_size return brange, residual_scale_factor def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None): if scaling_vector is None: x_flat = x.flatten(1) residual = residual.flatten(1) x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) else: x_plus_residual = scaled_index_add( x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor ) return x_plus_residual attn_bias_cache: Dict[Tuple, Any] = {} def get_attn_bias_and_cat(x_list, branges=None): """ this will perform the index select, cat the tensors, and provide the attn_bias from cache """ batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list] all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list)) if all_shapes not in attn_bias_cache.keys(): seqlens = [] for b, x in zip(batch_sizes, x_list): for _ in range(b): seqlens.append(x.shape[1]) attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens) attn_bias._batch_sizes = batch_sizes attn_bias_cache[all_shapes] = attn_bias if branges is not None: cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1]) else: tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list) cat_tensors = torch.cat(tensors_bs1, dim=1) return attn_bias_cache[all_shapes], cat_tensors def drop_add_residual_stochastic_depth_list( x_list: List[Tensor], residual_func: Callable[[Tensor, Any], Tensor], sample_drop_ratio: float = 0.0, scaling_vector=None, ) -> Tensor: # 1) generate random set of indices for dropping samples in the batch branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list] branges = [s[0] for s in branges_scales] residual_scale_factors = [s[1] for s in branges_scales] # 2) get attention bias and index+concat the tensors attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges) # 3) apply residual_func to get residual, and split the result residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore outputs = [] for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors): outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x)) return outputs class NestedTensorBlock(Block): def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: """ x_list contains a list of tensors to nest together and run """ assert isinstance(self.attn, MemEffAttention) if self.training and self.sample_drop_ratio > 0.0: def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: return self.attn(self.norm1(x), attn_bias=attn_bias) def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: return self.mlp(self.norm2(x)) x_list = drop_add_residual_stochastic_depth_list( x_list, residual_func=attn_residual_func, sample_drop_ratio=self.sample_drop_ratio, scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None, ) x_list = drop_add_residual_stochastic_depth_list( x_list, residual_func=ffn_residual_func, sample_drop_ratio=self.sample_drop_ratio, scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None, ) return x_list else: def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor: return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias)) def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor: return self.ls2(self.mlp(self.norm2(x))) attn_bias, x = get_attn_bias_and_cat(x_list) x = x + attn_residual_func(x, attn_bias=attn_bias) x = x + ffn_residual_func(x) return attn_bias.split(x) def forward(self, x_or_x_list): if isinstance(x_or_x_list, Tensor): return super().forward(x_or_x_list) elif isinstance(x_or_x_list, list): if not XFORMERS_AVAILABLE: raise AssertionError("xFormers is required for using nested tensors") return self.forward_nested(x_or_x_list) else: raise AssertionError ================================================ FILE: dinov2/layers/dino_head.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn from torch.nn.init import trunc_normal_ from torch.nn.utils import weight_norm class DINOHead(nn.Module): def __init__( self, in_dim, out_dim, use_bn=False, nlayers=3, hidden_dim=2048, bottleneck_dim=256, mlp_bias=True, ): super().__init__() nlayers = max(nlayers, 1) self.mlp = _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=hidden_dim, use_bn=use_bn, bias=mlp_bias) self.apply(self._init_weights) self.last_layer = weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False)) self.last_layer.weight_g.data.fill_(1) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) def forward(self, x): x = self.mlp(x) eps = 1e-6 if x.dtype == torch.float16 else 1e-12 x = nn.functional.normalize(x, dim=-1, p=2, eps=eps) x = self.last_layer(x) return x def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True): if nlayers == 1: return nn.Linear(in_dim, bottleneck_dim, bias=bias) else: layers = [nn.Linear(in_dim, hidden_dim, bias=bias)] if use_bn: layers.append(nn.BatchNorm1d(hidden_dim)) layers.append(nn.GELU()) for _ in range(nlayers - 2): layers.append(nn.Linear(hidden_dim, hidden_dim, bias=bias)) if use_bn: layers.append(nn.BatchNorm1d(hidden_dim)) layers.append(nn.GELU()) layers.append(nn.Linear(hidden_dim, bottleneck_dim, bias=bias)) return nn.Sequential(*layers) ================================================ FILE: dinov2/layers/drop_path.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py # https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py from torch import nn def drop_path(x, drop_prob: float = 0.0, training: bool = False): if drop_prob == 0.0 or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0: random_tensor.div_(keep_prob) output = x * random_tensor return output class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) ================================================ FILE: dinov2/layers/layer_scale.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110 from typing import Optional, Union import torch from torch import Tensor from torch import nn class LayerScale(nn.Module): def __init__( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = False, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ) -> None: super().__init__() self.inplace = inplace self.init_values = init_values self.gamma = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) self.reset_parameters() def reset_parameters(self): nn.init.constant_(self.gamma, self.init_values) def forward(self, x: Tensor) -> Tensor: return x.mul_(self.gamma) if self.inplace else x * self.gamma ================================================ FILE: dinov2/layers/mlp.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py # https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py from typing import Callable, Optional from torch import Tensor, nn class Mlp(nn.Module): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = nn.GELU, drop: float = 0.0, bias: bool = True, ) -> None: super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features, bias=bias) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features, bias=bias) self.drop = nn.Dropout(drop) def forward(self, x: Tensor) -> Tensor: x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x ================================================ FILE: dinov2/layers/patch_embed.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py # https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py from typing import Callable, Optional, Tuple, Union from torch import Tensor import torch.nn as nn def make_2tuple(x): if isinstance(x, tuple): assert len(x) == 2 return x assert isinstance(x, int) return (x, x) class PatchEmbed(nn.Module): """ 2D image to patch embedding: (B,C,H,W) -> (B,N,D) Args: img_size: Image size. patch_size: Patch token size. in_chans: Number of input image channels. embed_dim: Number of linear projection output channels. norm_layer: Normalization layer. """ def __init__( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int, int]] = 16, in_chans: int = 3, embed_dim: int = 768, norm_layer: Optional[Callable] = None, flatten_embedding: bool = True, ) -> None: super().__init__() image_HW = make_2tuple(img_size) patch_HW = make_2tuple(patch_size) patch_grid_size = ( image_HW[0] // patch_HW[0], image_HW[1] // patch_HW[1], ) self.img_size = image_HW self.patch_size = patch_HW self.patches_resolution = patch_grid_size self.num_patches = patch_grid_size[0] * patch_grid_size[1] self.in_chans = in_chans self.embed_dim = embed_dim self.flatten_embedding = flatten_embedding self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x: Tensor) -> Tensor: _, _, H, W = x.shape patch_H, patch_W = self.patch_size assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" x = self.proj(x) # B C H W H, W = x.size(2), x.size(3) x = x.flatten(2).transpose(1, 2) # B HW C x = self.norm(x) if not self.flatten_embedding: x = x.reshape(-1, H, W, self.embed_dim) # B H W C return x def flops(self) -> float: Ho, Wo = self.patches_resolution flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) if self.norm is not None: flops += Ho * Wo * self.embed_dim return flops ================================================ FILE: dinov2/layers/swiglu_ffn.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import os from typing import Callable, Optional import warnings from torch import Tensor, nn import torch.nn.functional as F class SwiGLUFFN(nn.Module): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = None, drop: float = 0.0, bias: bool = True, ) -> None: super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias) self.w3 = nn.Linear(hidden_features, out_features, bias=bias) def forward(self, x: Tensor) -> Tensor: x12 = self.w12(x) x1, x2 = x12.chunk(2, dim=-1) hidden = F.silu(x1) * x2 return self.w3(hidden) XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None try: if XFORMERS_ENABLED: from xformers.ops import SwiGLU XFORMERS_AVAILABLE = True warnings.warn("xFormers is available (SwiGLU)") else: warnings.warn("xFormers is disabled (SwiGLU)") raise ImportError except ImportError: SwiGLU = SwiGLUFFN XFORMERS_AVAILABLE = False warnings.warn("xFormers is not available (SwiGLU)") class SwiGLUFFNFused(SwiGLU): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = None, drop: float = 0.0, bias: bool = True, ) -> None: out_features = out_features or in_features hidden_features = hidden_features or in_features hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 super().__init__( in_features=in_features, hidden_features=hidden_features, out_features=out_features, bias=bias, ) class SwiGLUFFNAligned(nn.Module): def __init__( self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, act_layer: Callable[..., nn.Module] = nn.GELU, drop: float = 0.0, bias: bool = True, align_to: int = 8, device=None, ) -> None: super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features d = int(hidden_features * 2 / 3) swiglu_hidden_features = d + (-d % align_to) self.w1 = nn.Linear(in_features, swiglu_hidden_features, bias=bias, device=device) self.w2 = nn.Linear(in_features, swiglu_hidden_features, bias=bias, device=device) self.w3 = nn.Linear(swiglu_hidden_features, out_features, bias=bias, device=device) def forward(self, x: Tensor) -> Tensor: x1 = self.w1(x) x2 = self.w2(x) hidden = F.silu(x1) * x2 return self.w3(hidden) ================================================ FILE: dinov2/logging/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import functools import logging import os import sys from typing import Optional import dinov2.distributed as distributed from .helpers import MetricLogger, SmoothedValue # So that calling _configure_logger multiple times won't add many handlers @functools.lru_cache() def _configure_logger( name: Optional[str] = None, *, level: int = logging.DEBUG, output: Optional[str] = None, ): """ Configure a logger. Adapted from Detectron2. Args: name: The name of the logger to configure. level: The logging level to use. output: A file name or a directory to save log. If None, will not save log file. If ends with ".txt" or ".log", assumed to be a file name. Otherwise, logs will be saved to `output/log.txt`. Returns: The configured logger. """ logger = logging.getLogger(name) logger.setLevel(level) logger.propagate = False # Loosely match Google glog format: # [IWEF]yyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg # but use a shorter timestamp and include the logger name: # [IWEF]yyyymmdd hh:mm:ss logger threadid file:line] msg fmt_prefix = "%(levelname).1s%(asctime)s %(process)s %(name)s %(filename)s:%(lineno)s] " fmt_message = "%(message)s" fmt = fmt_prefix + fmt_message datefmt = "%Y%m%d %H:%M:%S" formatter = logging.Formatter(fmt=fmt, datefmt=datefmt) # stdout logging for main worker only if distributed.is_main_process(): handler = logging.StreamHandler(stream=sys.stdout) handler.setLevel(logging.DEBUG) handler.setFormatter(formatter) logger.addHandler(handler) # file logging for all workers if output: if os.path.splitext(output)[-1] in (".txt", ".log"): filename = output else: filename = os.path.join(output, "logs", "log.txt") if not distributed.is_main_process(): global_rank = distributed.get_global_rank() filename = filename + ".rank{}".format(global_rank) os.makedirs(os.path.dirname(filename), exist_ok=True) handler = logging.StreamHandler(open(filename, "a")) handler.setLevel(logging.DEBUG) handler.setFormatter(formatter) logger.addHandler(handler) return logger def setup_logging( output: Optional[str] = None, *, name: Optional[str] = None, level: int = logging.DEBUG, capture_warnings: bool = True, ) -> None: """ Setup logging. Args: output: A file name or a directory to save log files. If None, log files will not be saved. If output ends with ".txt" or ".log", it is assumed to be a file name. Otherwise, logs will be saved to `output/log.txt`. name: The name of the logger to configure, by default the root logger. level: The logging level to use. capture_warnings: Whether warnings should be captured as logs. """ logging.captureWarnings(capture_warnings) _configure_logger(name, level=level, output=output) ================================================ FILE: dinov2/logging/helpers.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from collections import defaultdict, deque import datetime import json import logging import time import torch import dinov2.distributed as distributed logger = logging.getLogger("dinov2") class MetricLogger(object): def __init__(self, delimiter="\t", output_file=None): self.meters = defaultdict(SmoothedValue) self.delimiter = delimiter self.output_file = output_file def update(self, **kwargs): for k, v in kwargs.items(): if isinstance(v, torch.Tensor): v = v.item() assert isinstance(v, (float, int)) self.meters[k].update(v) def __getattr__(self, attr): if attr in self.meters: return self.meters[attr] if attr in self.__dict__: return self.__dict__[attr] raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) def __str__(self): loss_str = [] for name, meter in self.meters.items(): loss_str.append("{}: {}".format(name, str(meter))) return self.delimiter.join(loss_str) def synchronize_between_processes(self): for meter in self.meters.values(): meter.synchronize_between_processes() def add_meter(self, name, meter): self.meters[name] = meter def dump_in_output_file(self, iteration, iter_time, data_time): if self.output_file is None or not distributed.is_main_process(): return dict_to_dump = dict( iteration=iteration, iter_time=iter_time, data_time=data_time, ) dict_to_dump.update({k: v.median for k, v in self.meters.items()}) with open(self.output_file, "a") as f: f.write(json.dumps(dict_to_dump) + "\n") pass def log_every(self, iterable, print_freq, header=None, n_iterations=None, start_iteration=0): i = start_iteration if not header: header = "" start_time = time.time() end = time.time() iter_time = SmoothedValue(fmt="{avg:.6f}") data_time = SmoothedValue(fmt="{avg:.6f}") if n_iterations is None: n_iterations = len(iterable) space_fmt = ":" + str(len(str(n_iterations))) + "d" log_list = [ header, "[{0" + space_fmt + "}/{1}]", "eta: {eta}", "{meters}", "time: {time}", "data: {data}", ] if torch.cuda.is_available(): log_list += ["max mem: {memory:.0f}"] log_msg = self.delimiter.join(log_list) MB = 1024.0 * 1024.0 for obj in iterable: data_time.update(time.time() - end) yield obj iter_time.update(time.time() - end) if i % print_freq == 0 or i == n_iterations - 1: self.dump_in_output_file(iteration=i, iter_time=iter_time.avg, data_time=data_time.avg) eta_seconds = iter_time.global_avg * (n_iterations - i) eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) if torch.cuda.is_available(): logger.info( log_msg.format( i, n_iterations, eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time), memory=torch.cuda.max_memory_allocated() / MB, ) ) else: logger.info( log_msg.format( i, n_iterations, eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time), ) ) i += 1 end = time.time() if i >= n_iterations: break total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) logger.info("{} Total time: {} ({:.6f} s / it)".format(header, total_time_str, total_time / n_iterations)) class SmoothedValue: """Track a series of values and provide access to smoothed values over a window or the global series average. """ def __init__(self, window_size=20, fmt=None): if fmt is None: fmt = "{median:.4f} ({global_avg:.4f})" self.deque = deque(maxlen=window_size) self.total = 0.0 self.count = 0 self.fmt = fmt def update(self, value, num=1): self.deque.append(value) self.count += num self.total += value * num def synchronize_between_processes(self): """ Distributed synchronization of the metric Warning: does not synchronize the deque! """ if not distributed.is_enabled(): return t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda") torch.distributed.barrier() torch.distributed.all_reduce(t) t = t.tolist() self.count = int(t[0]) self.total = t[1] @property def median(self): d = torch.tensor(list(self.deque)) return d.median().item() @property def avg(self): d = torch.tensor(list(self.deque), dtype=torch.float32) return d.mean().item() @property def global_avg(self): return self.total / self.count @property def max(self): return max(self.deque) @property def value(self): return self.deque[-1] def __str__(self): return self.fmt.format( median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value, ) ================================================ FILE: dinov2/loss/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .dino_clstoken_loss import DINOLoss from .ibot_patch_loss import iBOTPatchLoss from .koleo_loss import KoLeoLoss ================================================ FILE: dinov2/loss/dino_clstoken_loss.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.distributed as dist import torch.nn.functional as F from torch import nn class DINOLoss(nn.Module): def __init__( self, out_dim, student_temp=0.1, center_momentum=0.9, ): super().__init__() self.student_temp = student_temp self.center_momentum = center_momentum self.register_buffer("center", torch.zeros(1, out_dim)) self.updated = True self.reduce_handle = None self.len_teacher_output = None self.async_batch_center = None @torch.no_grad() def softmax_center_teacher(self, teacher_output, teacher_temp): self.apply_center_update() # teacher centering and sharpening return F.softmax((teacher_output - self.center) / teacher_temp, dim=-1) @torch.no_grad() def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_iterations=3): teacher_output = teacher_output.float() world_size = dist.get_world_size() if dist.is_initialized() else 1 Q = torch.exp(teacher_output / teacher_temp).t() # Q is K-by-B for consistency with notations from our paper B = Q.shape[1] * world_size # number of samples to assign K = Q.shape[0] # how many prototypes # make the matrix sums to 1 sum_Q = torch.sum(Q) if dist.is_initialized(): dist.all_reduce(sum_Q) Q /= sum_Q for it in range(n_iterations): # normalize each row: total weight per prototype must be 1/K sum_of_rows = torch.sum(Q, dim=1, keepdim=True) if dist.is_initialized(): dist.all_reduce(sum_of_rows) Q /= sum_of_rows Q /= K # normalize each column: total weight per sample must be 1/B Q /= torch.sum(Q, dim=0, keepdim=True) Q /= B Q *= B # the columns must sum to 1 so that Q is an assignment return Q.t() def forward(self, student_output_list, teacher_out_softmaxed_centered_list): """ Cross-entropy between softmax outputs of the teacher and student networks. """ # TODO: Use cross_entropy_distribution here total_loss = 0 for s in student_output_list: lsm = F.log_softmax(s / self.student_temp, dim=-1) for t in teacher_out_softmaxed_centered_list: loss = torch.sum(t * lsm, dim=-1) total_loss -= loss.mean() return total_loss @torch.no_grad() def update_center(self, teacher_output): self.reduce_center_update(teacher_output) @torch.no_grad() def reduce_center_update(self, teacher_output): self.updated = False self.len_teacher_output = len(teacher_output) self.async_batch_center = torch.sum(teacher_output, dim=0, keepdim=True) if dist.is_initialized(): self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True) @torch.no_grad() def apply_center_update(self): if self.updated is False: world_size = dist.get_world_size() if dist.is_initialized() else 1 if self.reduce_handle is not None: self.reduce_handle.wait() _t = self.async_batch_center / (self.len_teacher_output * world_size) self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum) self.updated = True ================================================ FILE: dinov2/loss/ibot_patch_loss.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import torch import torch.distributed as dist import torch.nn.functional as F from torch import nn import logging logger = logging.getLogger("dinov2") try: from xformers.ops import cross_entropy def lossfunc(t, s, temp): s = s.float() t = t.float() if s.ndim == 2: return -cross_entropy(s.unsqueeze(0), t.unsqueeze(0), temp, bw_inplace=True).squeeze(0) elif s.ndim == 3: return -cross_entropy(s, t, temp, bw_inplace=True) except ImportError: def lossfunc(t, s, temp): return torch.sum(t * F.log_softmax(s / temp, dim=-1), dim=-1) class iBOTPatchLoss(nn.Module): def __init__(self, patch_out_dim, student_temp=0.1, center_momentum=0.9): super().__init__() self.student_temp = student_temp self.center_momentum = center_momentum self.register_buffer("center", torch.zeros(1, 1, patch_out_dim)) self.updated = True self.reduce_handle = None self.len_teacher_patch_tokens = None self.async_batch_center = None @torch.no_grad() def softmax_center_teacher(self, teacher_patch_tokens, teacher_temp): self.apply_center_update() # teacher centering and sharpening # # WARNING: # as self.center is a float32, everything gets casted to float32 afterwards # # teacher_patch_tokens = teacher_patch_tokens.float() # return F.softmax((teacher_patch_tokens.sub_(self.center.to(teacher_patch_tokens.dtype))).mul_(1 / teacher_temp), dim=-1) return F.softmax((teacher_patch_tokens - self.center) / teacher_temp, dim=-1) # this is experimental, keep everything in float16 and let's see what happens: # return F.softmax((teacher_patch_tokens.sub_(self.center)) / teacher_temp, dim=-1) @torch.no_grad() def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_masked_patches_tensor, n_iterations=3): teacher_output = teacher_output.float() # world_size = dist.get_world_size() if dist.is_initialized() else 1 Q = torch.exp(teacher_output / teacher_temp).t() # Q is K-by-B for consistency with notations from our paper # B = Q.shape[1] * world_size # number of samples to assign B = n_masked_patches_tensor dist.all_reduce(B) K = Q.shape[0] # how many prototypes # make the matrix sums to 1 sum_Q = torch.sum(Q) if dist.is_initialized(): dist.all_reduce(sum_Q) Q /= sum_Q for it in range(n_iterations): # normalize each row: total weight per prototype must be 1/K sum_of_rows = torch.sum(Q, dim=1, keepdim=True) if dist.is_initialized(): dist.all_reduce(sum_of_rows) Q /= sum_of_rows Q /= K # normalize each column: total weight per sample must be 1/B Q /= torch.sum(Q, dim=0, keepdim=True) Q /= B Q *= B # the columns must sum to 1 so that Q is an assignment return Q.t() def forward(self, student_patch_tokens, teacher_patch_tokens, student_masks_flat): """ Cross-entropy between softmax outputs of the teacher and student networks. student_patch_tokens: (B, N, D) tensor teacher_patch_tokens: (B, N, D) tensor student_masks_flat: (B, N) tensor """ t = teacher_patch_tokens s = student_patch_tokens loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1) loss = torch.sum(loss * student_masks_flat.float(), dim=-1) / student_masks_flat.sum(dim=-1).clamp(min=1.0) return -loss.mean() def forward_masked( self, student_patch_tokens_masked, teacher_patch_tokens_masked, student_masks_flat, n_masked_patches=None, masks_weight=None, ): t = teacher_patch_tokens_masked s = student_patch_tokens_masked # loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1) loss = lossfunc(t, s, self.student_temp) if masks_weight is None: masks_weight = ( (1 / student_masks_flat.sum(-1).clamp(min=1.0)) .unsqueeze(-1) .expand_as(student_masks_flat)[student_masks_flat] ) if n_masked_patches is not None: loss = loss[:n_masked_patches] loss = loss * masks_weight return -loss.sum() / student_masks_flat.shape[0] @torch.no_grad() def update_center(self, teacher_patch_tokens): self.reduce_center_update(teacher_patch_tokens) @torch.no_grad() def reduce_center_update(self, teacher_patch_tokens): self.updated = False self.len_teacher_patch_tokens = len(teacher_patch_tokens) self.async_batch_center = torch.sum(teacher_patch_tokens.mean(1), dim=0, keepdim=True) if dist.is_initialized(): self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True) @torch.no_grad() def apply_center_update(self): if self.updated is False: world_size = dist.get_world_size() if dist.is_initialized() else 1 if self.reduce_handle is not None: self.reduce_handle.wait() _t = self.async_batch_center / (self.len_teacher_patch_tokens * world_size) self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum) self.updated = True ================================================ FILE: dinov2/loss/koleo_loss.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging import torch import torch.nn as nn import torch.nn.functional as F # import torch.distributed as dist logger = logging.getLogger("dinov2") class KoLeoLoss(nn.Module): """Kozachenko-Leonenko entropic loss regularizer from Sablayrolles et al. - 2018 - Spreading vectors for similarity search""" def __init__(self): super().__init__() self.pdist = nn.PairwiseDistance(2, eps=1e-8) def pairwise_NNs_inner(self, x): """ Pairwise nearest neighbors for L2-normalized vectors. Uses Torch rather than Faiss to remain on GPU. """ # parwise dot products (= inverse distance) dots = torch.mm(x, x.t()) n = x.shape[0] dots.view(-1)[:: (n + 1)].fill_(-1) # Trick to fill diagonal with -1 # max inner prod -> min distance _, I = torch.max(dots, dim=1) # noqa: E741 return I def forward(self, student_output, eps=1e-8): """ Args: student_output (BxD): backbone output of student """ with torch.cuda.amp.autocast(enabled=False): student_output = F.normalize(student_output, eps=eps, p=2, dim=-1) I = self.pairwise_NNs_inner(student_output) # noqa: E741 distances = self.pdist(student_output, student_output[I]) # BxD, BxD -> B loss = -torch.log(distances + eps).mean() return loss ================================================ FILE: dinov2/models/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging from . import vision_transformer as vits logger = logging.getLogger("dinov2") def build_model(args, only_teacher=False, img_size=224): args.arch = args.arch.removesuffix("_memeff") if "vit" in args.arch: vit_kwargs = dict( img_size=img_size, patch_size=args.patch_size, init_values=args.layerscale, ffn_layer=args.ffn_layer, block_chunks=args.block_chunks, qkv_bias=args.qkv_bias, proj_bias=args.proj_bias, ffn_bias=args.ffn_bias, num_register_tokens=args.num_register_tokens, interpolate_offset=args.interpolate_offset, interpolate_antialias=args.interpolate_antialias, in_chans=args.in_chans, channel_adaptive=args.channel_adaptive, ) teacher = vits.__dict__[args.arch](**vit_kwargs) if only_teacher: return teacher, teacher.embed_dim student = vits.__dict__[args.arch]( **vit_kwargs, drop_path_rate=args.drop_path_rate, drop_path_uniform=args.drop_path_uniform, ) embed_dim = student.embed_dim return student, teacher, embed_dim def build_model_from_cfg(cfg, only_teacher=False): return build_model(cfg.student, only_teacher=only_teacher, img_size=cfg.crops.global_crops_size) ================================================ FILE: dinov2/models/vision_transformer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # References: # https://github.com/facebookresearch/dino/blob/main/vision_transformer.py # https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py from functools import partial import math import logging from typing import Sequence, Tuple, Union, Callable import numpy as np import torch import torch.nn as nn import torch.utils.checkpoint from torch.nn.init import trunc_normal_ from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block logger = logging.getLogger("dinov2") def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module: if not depth_first and include_root: fn(module=module, name=name) for child_name, child_module in module.named_children(): child_name = ".".join((name, child_name)) if name else child_name named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True) if depth_first and include_root: fn(module=module, name=name) return module class BlockChunk(nn.ModuleList): def forward(self, x): for b in self: x = b(x) return x class DinoVisionTransformer(nn.Module): def __init__( self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True, ffn_bias=True, proj_bias=True, drop_path_rate=0.0, drop_path_uniform=False, init_values=None, # for layerscale: None or 0 => no layerscale embed_layer=PatchEmbed, act_layer=nn.GELU, block_fn=Block, ffn_layer="mlp", block_chunks=1, num_register_tokens=0, interpolate_antialias=False, interpolate_offset=0.1, channel_adaptive=False, ): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True proj_bias (bool): enable bias for proj in attn if True ffn_bias (bool): enable bias for ffn if True drop_path_rate (float): stochastic depth rate drop_path_uniform (bool): apply uniform drop rate across blocks weight_init (str): weight init scheme init_values (float): layer-scale init values embed_layer (nn.Module): patch embedding layer act_layer (nn.Module): MLP activation layer block_fn (nn.Module): transformer block class ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity" block_chunks: (int) split block sequence into block_chunks units for FSDP wrap num_register_tokens: (int) number of extra cls tokens (so-called "registers") interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings """ super().__init__() norm_layer = partial(nn.LayerNorm, eps=1e-6) self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_tokens = 1 self.n_blocks = depth self.num_heads = num_heads self.patch_size = patch_size self.num_register_tokens = num_register_tokens self.interpolate_antialias = interpolate_antialias self.interpolate_offset = interpolate_offset self.bag_of_channels = channel_adaptive self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim)) assert num_register_tokens >= 0 self.register_tokens = ( nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None ) if drop_path_uniform is True: dpr = [drop_path_rate] * depth else: dpr = np.linspace(0, drop_path_rate, depth).tolist() # stochastic depth decay rule if ffn_layer == "mlp": logger.info("using MLP layer as FFN") ffn_layer = Mlp elif ffn_layer == "swiglufused" or ffn_layer == "swiglu": logger.info("using SwiGLU layer as FFN") ffn_layer = SwiGLUFFNFused elif ffn_layer == "identity": logger.info("using Identity layer as FFN") def f(*args, **kwargs): return nn.Identity() ffn_layer = f else: raise NotImplementedError blocks_list = [ block_fn( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, proj_bias=proj_bias, ffn_bias=ffn_bias, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer, ffn_layer=ffn_layer, init_values=init_values, ) for i in range(depth) ] if block_chunks > 0: self.chunked_blocks = True chunked_blocks = [] chunksize = depth // block_chunks for i in range(0, depth, chunksize): # this is to keep the block index consistent if we chunk the block list chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize]) self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks]) else: self.chunked_blocks = False self.blocks = nn.ModuleList(blocks_list) self.norm = norm_layer(embed_dim) self.head = nn.Identity() self.mask_token = nn.Parameter(torch.zeros(1, embed_dim)) self.init_weights() def init_weights(self): trunc_normal_(self.pos_embed, std=0.02) nn.init.normal_(self.cls_token, std=1e-6) if self.register_tokens is not None: nn.init.normal_(self.register_tokens, std=1e-6) named_apply(init_weights_vit_timm, self) def interpolate_pos_encoding(self, x, w, h): previous_dtype = x.dtype npatch = x.shape[1] - 1 N = self.pos_embed.shape[1] - 1 if npatch == N and w == h: return self.pos_embed pos_embed = self.pos_embed.float() class_pos_embed = pos_embed[:, 0] patch_pos_embed = pos_embed[:, 1:] dim = x.shape[-1] w0 = w // self.patch_size h0 = h // self.patch_size M = int(math.sqrt(N)) # Recover the number of patches in each dimension assert N == M * M kwargs = {} if self.interpolate_offset: # Historical kludge: add a small number to avoid floating point error in the interpolation, see https://github.com/facebookresearch/dino/issues/8 # Note: still needed for backward-compatibility, the underlying operators are using both output size and scale factors sx = float(w0 + self.interpolate_offset) / M sy = float(h0 + self.interpolate_offset) / M kwargs["scale_factor"] = (sx, sy) else: # Simply specify an output size instead of a scale factor kwargs["size"] = (w0, h0) patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape(1, M, M, dim).permute(0, 3, 1, 2), mode="bicubic", antialias=self.interpolate_antialias, **kwargs, ) assert (w0, h0) == patch_pos_embed.shape[-2:] patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype) def prepare_tokens_with_masks(self, x, masks=None): B, nc, w, h = x.shape x = self.patch_embed(x) if masks is not None: x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x) x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) x = x + self.interpolate_pos_encoding(x, w, h) if self.register_tokens is not None: x = torch.cat( ( x[:, :1], self.register_tokens.expand(x.shape[0], -1, -1), x[:, 1:], ), dim=1, ) return x def forward_features_list(self, x_list, masks_list): x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)] for blk in self.blocks: x = blk(x) all_x = x output = [] for x, masks in zip(all_x, masks_list): x_norm = self.norm(x) output.append( { "x_norm_clstoken": x_norm[:, 0], "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], "x_prenorm": x, "masks": masks, } ) return output def forward_features(self, x, masks=None): if isinstance(x, list): return self.forward_features_list(x, masks) x = self.prepare_tokens_with_masks(x, masks) for blk in self.blocks: x = blk(x) x_norm = self.norm(x) return { "x_norm_clstoken": x_norm[:, 0], "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1], "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :], "x_prenorm": x, "masks": masks, } def _get_intermediate_layers_not_chunked(self, x, n=1): x = self.prepare_tokens_with_masks(x) # If n is an int, take the n last blocks. If it's a list, take them output, total_block_len = [], len(self.blocks) blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n for i, blk in enumerate(self.blocks): x = blk(x) if i in blocks_to_take: output.append(x) assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" return output def _get_intermediate_layers_chunked(self, x, n=1): x = self.prepare_tokens_with_masks(x) output, i, total_block_len = [], 0, len(self.blocks[-1]) # If n is an int, take the n last blocks. If it's a list, take them blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n for block_chunk in self.blocks: for blk in block_chunk[i:]: # Passing the nn.Identity() x = blk(x) if i in blocks_to_take: output.append(x) i += 1 assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found" return output def get_intermediate_layers( self, x: torch.Tensor, n: Union[int, Sequence] = 1, # Layers or n last layers to take reshape: bool = False, return_class_token: bool = False, norm=True, ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]: if self.bag_of_channels: B, C, H, W = x.shape x = x.reshape(B * C, 1, H, W) # passing channels to batch dimension to get encodings for each channel if self.chunked_blocks: outputs = self._get_intermediate_layers_chunked(x, n) else: outputs = self._get_intermediate_layers_not_chunked(x, n) if norm: outputs = [self.norm(out) for out in outputs] class_tokens = [out[:, 0] for out in outputs] outputs = [out[:, 1 + self.num_register_tokens :] for out in outputs] if reshape: B, _, w, h = x.shape outputs = [ out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous() for out in outputs ] if self.bag_of_channels: output = tuple(zip(outputs, class_tokens)) output = list( zip(*output) ) # unzip the tuple: (list of patch_tokens per block, list of class tokens per block) patch_tokens_per_block = output[0] # [BLOCK1, BLOCK2, ...] where BLOCK1.shape: B*C, N, D cls_tokens_per_block = output[1] # [BLOCK1, BLOCK2, ...] where BLOCK1.shape: B*C, D patch_tokens_per_block = [ patch_tokens.reshape(B, C, patch_tokens.shape[-2], patch_tokens.shape[-1]) for patch_tokens in patch_tokens_per_block ] # [BLOCK1, BLOCK2, ...] where BLOCK1.shape: B, C, N, D cls_tokens_per_block = [cls_tokens.reshape(B, -1) for cls_tokens in cls_tokens_per_block] output = tuple(zip(patch_tokens_per_block, cls_tokens_per_block)) return output if return_class_token: return tuple(zip(outputs, class_tokens)) return tuple(outputs) def forward(self, *args, is_training=False, **kwargs): ret = self.forward_features(*args, **kwargs) if is_training: return ret else: return self.head(ret["x_norm_clstoken"]) def init_weights_vit_timm(module: nn.Module, name: str = ""): """ViT weight initialization, original timm impl (for reproducibility)""" if isinstance(module, nn.Linear): trunc_normal_(module.weight, std=0.02) if module.bias is not None: nn.init.zeros_(module.bias) def vit_small(patch_size=16, num_register_tokens=0, in_chans=3, channel_adaptive=False, **kwargs): model = DinoVisionTransformer( patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, in_chans=in_chans, channel_adaptive=channel_adaptive, **kwargs, ) return model def vit_base(patch_size=16, num_register_tokens=0, in_chans=3, channel_adaptive=False, **kwargs): model = DinoVisionTransformer( patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, in_chans=in_chans, channel_adaptive=channel_adaptive, **kwargs, ) return model def vit_large(patch_size=16, num_register_tokens=0, in_chans=3, channel_adaptive=False, **kwargs): model = DinoVisionTransformer( patch_size=patch_size, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, in_chans=in_chans, channel_adaptive=channel_adaptive, **kwargs, ) return model def vit_giant2(patch_size=16, num_register_tokens=0, in_chans=3, channel_adaptive=False, **kwargs): """ Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 """ model = DinoVisionTransformer( patch_size=patch_size, embed_dim=1536, depth=40, num_heads=24, mlp_ratio=4, block_fn=partial(Block, attn_class=MemEffAttention), num_register_tokens=num_register_tokens, in_chans=in_chans, channel_adaptive=channel_adaptive, **kwargs, ) return model ================================================ FILE: dinov2/run/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. ================================================ FILE: dinov2/run/eval/cell_dino/knn.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import logging import os import sys from dinov2.eval.cell_dino.knn import get_args_parser as get_knn_args_parser from dinov2.logging import setup_logging from dinov2.run.submit import get_args_parser, submit_jobs logger = logging.getLogger("dinov2") class Evaluator: def __init__(self, args): self.args = args def __call__(self): from dinov2.eval.cell_dino.knn import main as knn_main self._setup_args() knn_main(self.args) def checkpoint(self): import submitit logger.info(f"Requeuing {self.args}") empty = type(self)(self.args) return submitit.helpers.DelayedSubmission(empty) def _setup_args(self): import submitit job_env = submitit.JobEnvironment() self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") logger.info(f"Args: {self.args}") def main(): description = "Submitit launcher for k-NN Cell-DINO and Channel-Adaptive DINO evaluation" knn_args_parser = get_knn_args_parser(add_help=False) parents = [knn_args_parser] args_parser = get_args_parser(description=description, parents=parents) args = args_parser.parse_args() setup_logging() assert os.path.exists(args.config_file), "Configuration file does not exist!" submit_jobs(Evaluator, args, name="dinov2:knn Cell-DINO") return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: dinov2/run/eval/cell_dino/linear.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. import logging import os import sys from dinov2.eval.cell_dino.linear import get_args_parser as get_linear_args_parser from dinov2.logging import setup_logging from dinov2.run.submit import get_args_parser, submit_jobs logger = logging.getLogger("dinov2") class Evaluator: def __init__(self, args): self.args = args def __call__(self): from dinov2.eval.cell_dino.linear import main as linear_main self._setup_args() linear_main(self.args) def checkpoint(self): import submitit logger.info(f"Requeuing {self.args}") empty = type(self)(self.args) return submitit.helpers.DelayedSubmission(empty) def _setup_args(self): import submitit job_env = submitit.JobEnvironment() self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") logger.info(f"Args: {self.args}") def main(): description = "Submitit launcher for DINOv2 linear Cell-DINO and Channel-Adaptive DINO evaluation" linear_args_parser = get_linear_args_parser(add_help=False) parents = [linear_args_parser] args_parser = get_args_parser(description=description, parents=parents) args = args_parser.parse_args() setup_logging() assert os.path.exists(args.config_file), "Configuration file does not exist!" submit_jobs(Evaluator, args, name="dinov2:linear Cell-DINO") return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: dinov2/run/eval/knn.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging import os import sys from dinov2.eval.knn import get_args_parser as get_knn_args_parser from dinov2.logging import setup_logging from dinov2.run.submit import get_args_parser, submit_jobs logger = logging.getLogger("dinov2") class Evaluator: def __init__(self, args): self.args = args def __call__(self): from dinov2.eval.knn import main as knn_main self._setup_args() knn_main(self.args) def checkpoint(self): import submitit logger.info(f"Requeuing {self.args}") empty = type(self)(self.args) return submitit.helpers.DelayedSubmission(empty) def _setup_args(self): import submitit job_env = submitit.JobEnvironment() self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") logger.info(f"Args: {self.args}") def main(): description = "Submitit launcher for DINOv2 k-NN evaluation" knn_args_parser = get_knn_args_parser(add_help=False) parents = [knn_args_parser] args_parser = get_args_parser(description=description, parents=parents) args = args_parser.parse_args() setup_logging() assert os.path.exists(args.config_file), "Configuration file does not exist!" submit_jobs(Evaluator, args, name="dinov2:knn") return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: dinov2/run/eval/linear.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging import os import sys from dinov2.eval.linear import get_args_parser as get_linear_args_parser from dinov2.logging import setup_logging from dinov2.run.submit import get_args_parser, submit_jobs logger = logging.getLogger("dinov2") class Evaluator: def __init__(self, args): self.args = args def __call__(self): from dinov2.eval.linear import main as linear_main self._setup_args() linear_main(self.args) def checkpoint(self): import submitit logger.info(f"Requeuing {self.args}") empty = type(self)(self.args) return submitit.helpers.DelayedSubmission(empty) def _setup_args(self): import submitit job_env = submitit.JobEnvironment() self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") logger.info(f"Args: {self.args}") def main(): description = "Submitit launcher for DINOv2 linear evaluation" linear_args_parser = get_linear_args_parser(add_help=False) parents = [linear_args_parser] args_parser = get_args_parser(description=description, parents=parents) args = args_parser.parse_args() setup_logging() assert os.path.exists(args.config_file), "Configuration file does not exist!" submit_jobs(Evaluator, args, name="dinov2:linear") return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: dinov2/run/eval/log_regression.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging import os import sys from dinov2.eval.log_regression import get_args_parser as get_log_regression_args_parser from dinov2.logging import setup_logging from dinov2.run.submit import get_args_parser, submit_jobs logger = logging.getLogger("dinov2") class Evaluator: def __init__(self, args): self.args = args def __call__(self): from dinov2.eval.log_regression import main as log_regression_main self._setup_args() log_regression_main(self.args) def checkpoint(self): import submitit logger.info(f"Requeuing {self.args}") empty = type(self)(self.args) return submitit.helpers.DelayedSubmission(empty) def _setup_args(self): import submitit job_env = submitit.JobEnvironment() self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") logger.info(f"Args: {self.args}") def main(): description = "Submitit launcher for DINOv2 logistic evaluation" log_regression_args_parser = get_log_regression_args_parser(add_help=False) parents = [log_regression_args_parser] args_parser = get_args_parser(description=description, parents=parents) args = args_parser.parse_args() setup_logging() assert os.path.exists(args.config_file), "Configuration file does not exist!" submit_jobs(Evaluator, args, name="dinov2:logreg") return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: dinov2/run/submit.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import argparse import logging import os from pathlib import Path from typing import List, Optional import submitit from dinov2.utils.cluster import ( get_slurm_executor_parameters, get_slurm_partition, get_user_checkpoint_path, ) logger = logging.getLogger("dinov2") def get_args_parser( description: Optional[str] = None, parents: Optional[List[argparse.ArgumentParser]] = None, add_help: bool = True, ) -> argparse.ArgumentParser: parents = parents or [] slurm_partition = get_slurm_partition() parser = argparse.ArgumentParser( description=description, parents=parents, add_help=add_help, ) parser.add_argument( "--ngpus", "--gpus", "--gpus-per-node", default=8, type=int, help="Number of GPUs to request on each node", ) parser.add_argument( "--nodes", "--nnodes", default=1, type=int, help="Number of nodes to request", ) parser.add_argument( "--timeout", default=2800, type=int, help="Duration of the job", ) parser.add_argument( "--partition", default=slurm_partition, type=str, help="Partition where to submit", ) parser.add_argument( "--use-volta32", action="store_true", help="Request V100-32GB GPUs", ) parser.add_argument( "--comment", default="", type=str, help="Comment to pass to scheduler, e.g. priority message", ) parser.add_argument( "--exclude", default="", type=str, help="Nodes to exclude", ) return parser def get_shared_folder() -> Path: user_checkpoint_path = get_user_checkpoint_path() if user_checkpoint_path is None: raise RuntimeError("Path to user checkpoint cannot be determined") path = user_checkpoint_path / "experiments" path.mkdir(exist_ok=True) return path def submit_jobs(task_class, args, name: str): if not args.output_dir: args.output_dir = str(get_shared_folder() / "%j") Path(args.output_dir).mkdir(parents=True, exist_ok=True) executor = submitit.AutoExecutor(folder=args.output_dir, slurm_max_num_timeout=30) kwargs = {} if args.use_volta32: kwargs["slurm_constraint"] = "volta32gb" if args.comment: kwargs["slurm_comment"] = args.comment if args.exclude: kwargs["slurm_exclude"] = args.exclude executor_params = get_slurm_executor_parameters( nodes=args.nodes, num_gpus_per_node=args.ngpus, timeout_min=args.timeout, # max is 60 * 72 slurm_signal_delay_s=120, slurm_partition=args.partition, **kwargs, ) executor.update_parameters(name=name, **executor_params) task = task_class(args) job = executor.submit(task) logger.info(f"Submitted job_id: {job.job_id}") str_output_dir = os.path.abspath(args.output_dir).replace("%j", str(job.job_id)) logger.info(f"Logs and checkpoints will be saved at: {str_output_dir}") ================================================ FILE: dinov2/run/train/train.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging import os import sys from dinov2.logging import setup_logging from dinov2.train import get_args_parser as get_train_args_parser from dinov2.run.submit import get_args_parser, submit_jobs logger = logging.getLogger("dinov2") class Trainer(object): def __init__(self, args): self.args = args def __call__(self): from dinov2.train import main as train_main self._setup_args() train_main(self.args) def checkpoint(self): import submitit logger.info(f"Requeuing {self.args}") empty = type(self)(self.args) return submitit.helpers.DelayedSubmission(empty) def _setup_args(self): import submitit job_env = submitit.JobEnvironment() self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id)) logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}") logger.info(f"Args: {self.args}") def main(): description = "Submitit launcher for DINOv2 training" train_args_parser = get_train_args_parser(add_help=False) parents = [train_args_parser] args_parser = get_args_parser(description=description, parents=parents) args = args_parser.parse_args() setup_logging() assert os.path.exists(args.config_file), "Configuration file does not exist!" submit_jobs(Trainer, args, name="dinov2:train") return 0 if __name__ == "__main__": sys.exit(main()) ================================================ FILE: dinov2/thirdparty/CLIP/LICENSE ================================================ MIT License Copyright (c) 2021 OpenAI 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. ================================================ FILE: dinov2/thirdparty/CLIP/clip/simple_tokenizer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import gzip import html import os from functools import lru_cache import ftfy import regex as re @lru_cache() def default_bpe(): return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a signficant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. """ bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() def whitespace_clean(text): text = re.sub(r"\s+", " ", text) text = text.strip() return text class SimpleTokenizer(object): def __init__(self, bpe_path: str = default_bpe()): self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} merges = gzip.open(bpe_path).read().decode("utf-8").split("\n") merges = merges[1 : 49152 - 256 - 2 + 1] merges = [tuple(merge.split()) for merge in merges] vocab = list(bytes_to_unicode().values()) vocab = vocab + [v + "" for v in vocab] for merge in merges: vocab.append("".join(merge)) vocab.extend(["<|startoftext|>", "<|endoftext|>"]) self.encoder = dict(zip(vocab, range(len(vocab)))) self.decoder = {v: k for k, v in self.encoder.items()} self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"} self.pat = re.compile( r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE, ) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token[:-1]) + (token[-1] + "",) pairs = get_pairs(word) if not pairs: return token + "" while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) new_word.extend(word[i:j]) i = j except Exception: new_word.extend(word[i:]) break if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word def encode(self, text): bpe_tokens = [] text = whitespace_clean(basic_clean(text)).lower() for token in re.findall(self.pat, text): token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")) return bpe_tokens def decode(self, tokens): text = "".join([self.decoder[token] for token in tokens]) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors="replace").replace("", " ") return text ================================================ FILE: dinov2/train/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from .train import get_args_parser, main from .ssl_meta_arch import SSLMetaArch ================================================ FILE: dinov2/train/ssl_meta_arch.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from functools import partial import logging import torch from torch import nn from dinov2.loss import DINOLoss, iBOTPatchLoss, KoLeoLoss from dinov2.models import build_model_from_cfg from dinov2.layers import DINOHead from dinov2.utils.utils import has_batchnorms from dinov2.utils.param_groups import get_params_groups_with_decay, fuse_params_groups from dinov2.fsdp import get_fsdp_wrapper, ShardedGradScaler, get_fsdp_modules, reshard_fsdp_model from dinov2.models.vision_transformer import BlockChunk try: from xformers.ops import fmha except ImportError: raise AssertionError("xFormers is required for training") logger = logging.getLogger("dinov2") class SSLMetaArch(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg self.fp16_scaler = ShardedGradScaler() if cfg.compute_precision.grad_scaler else None student_model_dict = dict() teacher_model_dict = dict() student_backbone, teacher_backbone, embed_dim = build_model_from_cfg(cfg) student_model_dict["backbone"] = student_backbone teacher_model_dict["backbone"] = teacher_backbone logger.info(f"OPTIONS -- architecture : embed_dim: {embed_dim}") if cfg.student.pretrained_weights: chkpt = torch.load(cfg.student.pretrained_weights) logger.info(f"OPTIONS -- pretrained weights: loading from {cfg.student.pretrained_weights}") student_backbone.load_state_dict(chkpt["model"], strict=False) self.embed_dim = embed_dim self.dino_out_dim = cfg.dino.head_n_prototypes self.do_dino = cfg.dino.loss_weight > 0 self.do_koleo = cfg.dino.koleo_loss_weight > 0 self.do_ibot = cfg.ibot.loss_weight > 0 self.ibot_separate_head = cfg.ibot.separate_head logger.info("OPTIONS -- DINO") if self.do_dino: logger.info(f"OPTIONS -- DINO -- loss_weight: {cfg.dino.loss_weight}") logger.info(f"OPTIONS -- DINO -- head_n_prototypes: {cfg.dino.head_n_prototypes}") logger.info(f"OPTIONS -- DINO -- head_bottleneck_dim: {cfg.dino.head_bottleneck_dim}") logger.info(f"OPTIONS -- DINO -- head_hidden_dim: {cfg.dino.head_hidden_dim}") self.dino_loss_weight = cfg.dino.loss_weight dino_head = partial( DINOHead, in_dim=embed_dim, out_dim=cfg.dino.head_n_prototypes, hidden_dim=cfg.dino.head_hidden_dim, bottleneck_dim=cfg.dino.head_bottleneck_dim, nlayers=cfg.dino.head_nlayers, ) self.dino_loss = DINOLoss(self.dino_out_dim) if self.do_koleo: logger.info("OPTIONS -- DINO -- applying KOLEO regularization") self.koleo_loss = KoLeoLoss() else: logger.info("OPTIONS -- DINO -- not using DINO") if self.do_dino or self.do_ibot: student_model_dict["dino_head"] = dino_head() teacher_model_dict["dino_head"] = dino_head() logger.info("OPTIONS -- IBOT") logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}") logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_ratio_tuple: {cfg.ibot.mask_ratio_min_max}") logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_sample_probability: {cfg.ibot.mask_sample_probability}") if self.do_ibot: self.ibot_loss_weight = cfg.ibot.loss_weight assert max(cfg.ibot.mask_ratio_min_max) > 0, "please provide a positive mask ratio tuple for ibot" assert cfg.ibot.mask_sample_probability > 0, "please provide a positive mask probability for ibot" self.ibot_out_dim = cfg.ibot.head_n_prototypes if self.ibot_separate_head else cfg.dino.head_n_prototypes self.ibot_patch_loss = iBOTPatchLoss(self.ibot_out_dim) if self.ibot_separate_head: logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}") logger.info(f"OPTIONS -- IBOT -- head_n_prototypes: {cfg.ibot.head_n_prototypes}") logger.info(f"OPTIONS -- IBOT -- head_bottleneck_dim: {cfg.ibot.head_bottleneck_dim}") logger.info(f"OPTIONS -- IBOT -- head_hidden_dim: {cfg.ibot.head_hidden_dim}") ibot_head = partial( DINOHead, in_dim=embed_dim, out_dim=cfg.ibot.head_n_prototypes, hidden_dim=cfg.ibot.head_hidden_dim, bottleneck_dim=cfg.ibot.head_bottleneck_dim, nlayers=cfg.ibot.head_nlayers, ) student_model_dict["ibot_head"] = ibot_head() teacher_model_dict["ibot_head"] = ibot_head() else: logger.info("OPTIONS -- IBOT -- head shared with DINO") self.need_to_synchronize_fsdp_streams = True self.student = nn.ModuleDict(student_model_dict) self.teacher = nn.ModuleDict(teacher_model_dict) # there is no backpropagation through the teacher, so no need for gradients for p in self.teacher.parameters(): p.requires_grad = False logger.info(f"Student and Teacher are built: they are both {cfg.student.arch} network.") def forward(self, inputs): raise NotImplementedError def backprop_loss(self, loss): if self.fp16_scaler is not None: self.fp16_scaler.scale(loss).backward() else: loss.backward() def forward_backward(self, images, teacher_temp): n_global_crops = 2 assert n_global_crops == 2 n_local_crops = self.cfg.crops.local_crops_number global_crops = images["collated_global_crops"].cuda(non_blocking=True) local_crops = images["collated_local_crops"].cuda(non_blocking=True) masks = images["collated_masks"].cuda(non_blocking=True) mask_indices_list = images["mask_indices_list"].cuda(non_blocking=True) n_masked_patches_tensor = images["n_masked_patches"].cuda(non_blocking=True) n_masked_patches = mask_indices_list.shape[0] upperbound = images["upperbound"] masks_weight = images["masks_weight"].cuda(non_blocking=True) n_local_crops_loss_terms = max(n_local_crops * n_global_crops, 1) n_global_crops_loss_terms = (n_global_crops - 1) * n_global_crops do_dino = self.do_dino do_ibot = self.do_ibot # loss scales ibot_loss_scale = 1.0 / n_global_crops # teacher output @torch.no_grad() def get_teacher_output(): x, n_global_crops_teacher = global_crops, n_global_crops teacher_backbone_output_dict = self.teacher.backbone(x, is_training=True) teacher_cls_tokens = teacher_backbone_output_dict["x_norm_clstoken"] teacher_cls_tokens = teacher_cls_tokens.chunk(n_global_crops_teacher) # watch out: these are chunked and cat'd in reverse so A is matched to B in the global crops dino loss teacher_cls_tokens = torch.cat((teacher_cls_tokens[1], teacher_cls_tokens[0])) ibot_teacher_patch_tokens = teacher_backbone_output_dict["x_norm_patchtokens"] _dim = ibot_teacher_patch_tokens.shape[-1] n_cls_tokens = teacher_cls_tokens.shape[0] if do_ibot and not self.ibot_separate_head: buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound + n_cls_tokens, _dim) buffer_tensor_teacher[:n_cls_tokens].copy_(teacher_cls_tokens) torch.index_select( ibot_teacher_patch_tokens.flatten(0, 1), dim=0, index=mask_indices_list, out=buffer_tensor_teacher[n_cls_tokens : n_cls_tokens + n_masked_patches], ) tokens_after_head = self.teacher.dino_head(buffer_tensor_teacher) teacher_cls_tokens_after_head = tokens_after_head[:n_cls_tokens] masked_teacher_patch_tokens_after_head = tokens_after_head[ n_cls_tokens : n_cls_tokens + n_masked_patches ] elif do_ibot and self.ibot_separate_head: buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound, _dim) torch.index_select( ibot_teacher_patch_tokens.flatten(0, 1), dim=0, index=mask_indices_list, out=buffer_tensor_teacher[:n_masked_patches], ) teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens) masked_teacher_patch_tokens_after_head = self.teacher.ibot_head(buffer_tensor_teacher)[ :n_masked_patches ] else: teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens) masked_teacher_ibot_softmaxed_centered = None if self.cfg.train.centering == "centering": teacher_dino_softmaxed_centered_list = self.dino_loss.softmax_center_teacher( teacher_cls_tokens_after_head, teacher_temp=teacher_temp ).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:]) self.dino_loss.update_center(teacher_cls_tokens_after_head) if do_ibot: masked_teacher_patch_tokens_after_head = masked_teacher_patch_tokens_after_head.unsqueeze(0) masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.softmax_center_teacher( masked_teacher_patch_tokens_after_head[:, :n_masked_patches], teacher_temp=teacher_temp ) masked_teacher_ibot_softmaxed_centered = masked_teacher_ibot_softmaxed_centered.squeeze(0) self.ibot_patch_loss.update_center(masked_teacher_patch_tokens_after_head[:n_masked_patches]) elif self.cfg.train.centering == "sinkhorn_knopp": teacher_dino_softmaxed_centered_list = self.dino_loss.sinkhorn_knopp_teacher( teacher_cls_tokens_after_head, teacher_temp=teacher_temp ).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:]) if do_ibot: masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.sinkhorn_knopp_teacher( masked_teacher_patch_tokens_after_head, teacher_temp=teacher_temp, n_masked_patches_tensor=n_masked_patches_tensor, ) else: raise NotImplementedError return teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered = get_teacher_output() reshard_fsdp_model(self.teacher) loss_dict = {} loss_accumulator = 0 # for backprop student_global_backbone_output_dict, student_local_backbone_output_dict = self.student.backbone( [global_crops, local_crops], masks=[masks, None], is_training=True ) inputs_for_student_head_list = [] # 1a: local crops cls tokens student_local_cls_tokens = student_local_backbone_output_dict["x_norm_clstoken"] inputs_for_student_head_list.append(student_local_cls_tokens.unsqueeze(0)) # 1b: global crops cls tokens student_global_cls_tokens = student_global_backbone_output_dict["x_norm_clstoken"] inputs_for_student_head_list.append(student_global_cls_tokens.unsqueeze(0)) # 1c: global crops patch tokens if do_ibot: _dim = student_global_backbone_output_dict["x_norm_clstoken"].shape[-1] ibot_student_patch_tokens = student_global_backbone_output_dict["x_norm_patchtokens"] buffer_tensor_patch_tokens = ibot_student_patch_tokens.new_zeros(upperbound, _dim) buffer_tensor_patch_tokens[:n_masked_patches].copy_( torch.index_select(ibot_student_patch_tokens.flatten(0, 1), dim=0, index=mask_indices_list) ) if not self.ibot_separate_head: inputs_for_student_head_list.append(buffer_tensor_patch_tokens.unsqueeze(0)) else: student_global_masked_patch_tokens_after_head = self.student.ibot_head(buffer_tensor_patch_tokens)[ :n_masked_patches ] # 2: run _attn_bias, cat_inputs = fmha.BlockDiagonalMask.from_tensor_list(inputs_for_student_head_list) outputs_list = _attn_bias.split(self.student.dino_head(cat_inputs)) # 3a: local crops cls tokens student_local_cls_tokens_after_head = outputs_list.pop(0).squeeze(0) # 3b: global crops cls tokens student_global_cls_tokens_after_head = outputs_list.pop(0).squeeze(0) # 3c: global crops patch tokens if do_ibot and not self.ibot_separate_head: student_global_masked_patch_tokens_after_head = outputs_list.pop(0).squeeze(0)[:n_masked_patches] if n_local_crops > 0: dino_local_crops_loss = self.dino_loss( student_output_list=student_local_cls_tokens_after_head.chunk(n_local_crops), teacher_out_softmaxed_centered_list=teacher_dino_softmaxed_centered_list, ) / (n_global_crops_loss_terms + n_local_crops_loss_terms) # store for display loss_dict["dino_local_crops_loss"] = dino_local_crops_loss # accumulate loss loss_accumulator += self.dino_loss_weight * dino_local_crops_loss # process global crops loss_scales = 2 # this is here since we process global crops together if do_dino: # compute loss dino_global_crops_loss = ( self.dino_loss( student_output_list=[student_global_cls_tokens_after_head], teacher_out_softmaxed_centered_list=[ teacher_dino_softmaxed_centered_list.flatten(0, 1) ], # these were chunked and stacked in reverse so A is matched to B ) * loss_scales / (n_global_crops_loss_terms + n_local_crops_loss_terms) ) loss_dict["dino_global_crops_loss"] = dino_global_crops_loss # accumulate loss loss_accumulator += self.dino_loss_weight * dino_global_crops_loss student_cls_tokens = student_global_cls_tokens if self.do_koleo: koleo_loss = self.cfg.dino.koleo_loss_weight * sum( self.koleo_loss(p) for p in student_cls_tokens.chunk(2) ) # we don't apply koleo loss between cls tokens of a same image loss_accumulator += koleo_loss loss_dict["koleo_loss"] = ( koleo_loss / loss_scales ) # this is to display the same losses as before but we can remove eventually if do_ibot: # compute loss ibot_patch_loss = ( self.ibot_patch_loss.forward_masked( student_global_masked_patch_tokens_after_head, masked_teacher_ibot_softmaxed_centered, student_masks_flat=masks, n_masked_patches=n_masked_patches, masks_weight=masks_weight, ) * loss_scales * ibot_loss_scale ) # store for display loss_dict["ibot_loss"] = ibot_patch_loss / 2 # accumulate loss loss_accumulator += self.ibot_loss_weight * ibot_patch_loss self.backprop_loss(loss_accumulator) self.fsdp_synchronize_streams() return loss_dict def fsdp_synchronize_streams(self): if self.need_to_synchronize_fsdp_streams: torch.cuda.synchronize() self.student.dino_head._streams = ( self.teacher.dino_head._streams ) = self.student.backbone._streams = self.teacher.backbone._streams self.need_to_synchronize_fsdp_streams = False def update_teacher(self, m): student_param_list = [] teacher_param_list = [] with torch.no_grad(): for k in self.student.keys(): for ms, mt in zip(get_fsdp_modules(self.student[k]), get_fsdp_modules(self.teacher[k])): student_param_list += ms.params teacher_param_list += mt.params torch._foreach_mul_(teacher_param_list, m) torch._foreach_add_(teacher_param_list, student_param_list, alpha=1 - m) def train(self): super().train() self.teacher.eval() def get_maybe_fused_params_for_submodel(self, m): params_groups = get_params_groups_with_decay( model=m, lr_decay_rate=self.cfg.optim.layerwise_decay, patch_embed_lr_mult=self.cfg.optim.patch_embed_lr_mult, ) fused_params_groups = fuse_params_groups(params_groups) logger.info("fusing param groups") for g in fused_params_groups: g["foreach"] = True return fused_params_groups def get_params_groups(self): all_params_groups = [] for m in self.student.values(): all_params_groups += self.get_maybe_fused_params_for_submodel(m) return all_params_groups def prepare_for_distributed_training(self): logger.info("DISTRIBUTED FSDP -- preparing model for distributed training") if has_batchnorms(self.student): raise NotImplementedError # below will synchronize all student subnetworks across gpus: for k, v in self.student.items(): self.teacher[k].load_state_dict(self.student[k].state_dict()) student_model_cfg = self.cfg.compute_precision.student[k] self.student[k] = get_fsdp_wrapper(student_model_cfg, modules_to_wrap={BlockChunk})(self.student[k]) teacher_model_cfg = self.cfg.compute_precision.teacher[k] self.teacher[k] = get_fsdp_wrapper(teacher_model_cfg, modules_to_wrap={BlockChunk})(self.teacher[k]) ================================================ FILE: dinov2/train/train.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import argparse import logging import math import os from functools import partial from fvcore.common.checkpoint import PeriodicCheckpointer import torch from dinov2.data import SamplerType, make_data_loader, make_dataset from dinov2.data import collate_data_and_cast, DataAugmentationDINO, CellAugmentationDINO, MaskingGenerator import dinov2.distributed as distributed from dinov2.fsdp import FSDPCheckpointer from dinov2.logging import MetricLogger from dinov2.utils.config import setup from dinov2.utils.utils import CosineScheduler from dinov2.train.ssl_meta_arch import SSLMetaArch torch.backends.cuda.matmul.allow_tf32 = True # PyTorch 1.12 sets this to False by default logger = logging.getLogger("dinov2") def get_args_parser(add_help: bool = True): parser = argparse.ArgumentParser("DINOv2 training", add_help=add_help) parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file") parser.add_argument( "--no-resume", action="store_true", help="Whether to not attempt to resume from the checkpoint directory. ", ) parser.add_argument("--eval-only", action="store_true", help="perform evaluation only") parser.add_argument("--eval", type=str, default="", help="Eval type to perform") parser.add_argument( "opts", help=""" Modify config options at the end of the command. For Yacs configs, use space-separated "PATH.KEY VALUE" pairs. For python-based LazyConfig, use "path.key=value". """.strip(), default=None, nargs=argparse.REMAINDER, ) parser.add_argument( "--output-dir", "--output_dir", default="", type=str, help="Output directory to save logs and checkpoints", ) return parser def build_optimizer(cfg, params_groups): return torch.optim.AdamW(params_groups, betas=(cfg.optim.adamw_beta1, cfg.optim.adamw_beta2)) def build_schedulers(cfg): OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH lr = dict( base_value=cfg.optim["lr"], final_value=cfg.optim["min_lr"], total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, warmup_iters=cfg.optim["warmup_epochs"] * OFFICIAL_EPOCH_LENGTH, start_warmup_value=0, ) wd = dict( base_value=cfg.optim["weight_decay"], final_value=cfg.optim["weight_decay_end"], total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, ) momentum = dict( base_value=cfg.teacher["momentum_teacher"], final_value=cfg.teacher["final_momentum_teacher"], total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH, ) teacher_temp = dict( base_value=cfg.teacher["teacher_temp"], final_value=cfg.teacher["teacher_temp"], total_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH, warmup_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH, start_warmup_value=cfg.teacher["warmup_teacher_temp"], ) lr_schedule = CosineScheduler(**lr) wd_schedule = CosineScheduler(**wd) momentum_schedule = CosineScheduler(**momentum) teacher_temp_schedule = CosineScheduler(**teacher_temp) last_layer_lr_schedule = CosineScheduler(**lr) last_layer_lr_schedule.schedule[ : cfg.optim["freeze_last_layer_epochs"] * OFFICIAL_EPOCH_LENGTH ] = 0 # mimicking the original schedules logger.info("Schedulers ready.") return ( lr_schedule, wd_schedule, momentum_schedule, teacher_temp_schedule, last_layer_lr_schedule, ) def apply_optim_scheduler(optimizer, lr, wd, last_layer_lr): for param_group in optimizer.param_groups: is_last_layer = param_group["is_last_layer"] lr_multiplier = param_group["lr_multiplier"] wd_multiplier = param_group["wd_multiplier"] param_group["weight_decay"] = wd * wd_multiplier param_group["lr"] = (last_layer_lr if is_last_layer else lr) * lr_multiplier def do_test(cfg, model, iteration): new_state_dict = model.teacher.state_dict() if distributed.is_main_process(): iterstring = str(iteration) eval_dir = os.path.join(cfg.train.output_dir, "eval", iterstring) os.makedirs(eval_dir, exist_ok=True) # save teacher checkpoint teacher_ckp_path = os.path.join(eval_dir, "teacher_checkpoint.pth") torch.save({"teacher": new_state_dict}, teacher_ckp_path) def do_train(cfg, model, resume=False): model.train() inputs_dtype = torch.half fp16_scaler = model.fp16_scaler # for mixed precision training # setup optimizer optimizer = build_optimizer(cfg, model.get_params_groups()) ( lr_schedule, wd_schedule, momentum_schedule, teacher_temp_schedule, last_layer_lr_schedule, ) = build_schedulers(cfg) # checkpointer checkpointer = FSDPCheckpointer(model, cfg.train.output_dir, optimizer=optimizer, save_to_disk=True) start_iter = checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1 OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH max_iter = cfg.optim.epochs * OFFICIAL_EPOCH_LENGTH periodic_checkpointer = PeriodicCheckpointer( checkpointer, period=3 * OFFICIAL_EPOCH_LENGTH, max_iter=max_iter, max_to_keep=3, ) # setup data preprocessing img_size = cfg.crops.global_crops_size patch_size = cfg.student.patch_size n_tokens = (img_size // patch_size) ** 2 mask_generator = MaskingGenerator( input_size=(img_size // patch_size, img_size // patch_size), max_num_patches=0.5 * img_size // patch_size * img_size // patch_size, ) if cfg.train.cell_augmentation: data_transform = CellAugmentationDINO( cfg.crops.global_crops_scale, cfg.crops.local_crops_scale, cfg.crops.local_crops_number, global_crops_size=cfg.crops.global_crops_size, local_crops_size=cfg.crops.local_crops_size, ) else: data_transform = DataAugmentationDINO( cfg.crops.global_crops_scale, cfg.crops.local_crops_scale, cfg.crops.local_crops_number, global_crops_size=cfg.crops.global_crops_size, local_crops_size=cfg.crops.local_crops_size, ) collate_fn = partial( collate_data_and_cast, mask_ratio_tuple=cfg.ibot.mask_ratio_min_max, mask_probability=cfg.ibot.mask_sample_probability, n_tokens=n_tokens, mask_generator=mask_generator, dtype=inputs_dtype, ) # setup data loader dataset = make_dataset( dataset_str=cfg.train.dataset_path, transform=data_transform, target_transform=lambda _: (), ) # sampler_type = SamplerType.INFINITE sampler_type = SamplerType.SHARDED_INFINITE data_loader = make_data_loader( dataset=dataset, batch_size=cfg.train.batch_size_per_gpu, num_workers=cfg.train.num_workers, shuffle=True, seed=start_iter, # TODO: Fix this -- cfg.train.seed sampler_type=sampler_type, sampler_advance=0, # TODO(qas): fix this -- start_iter * cfg.train.batch_size_per_gpu, drop_last=True, collate_fn=collate_fn, ) # training loop iteration = start_iter logger.info("Starting training from iteration {}".format(start_iter)) metrics_file = os.path.join(cfg.train.output_dir, "training_metrics.json") metric_logger = MetricLogger(delimiter=" ", output_file=metrics_file) header = "Training" for data in metric_logger.log_every( data_loader, 10, header, max_iter, start_iter, ): current_batch_size = data["collated_global_crops"].shape[0] / 2 if iteration > max_iter: return # apply schedules lr = lr_schedule[iteration] wd = wd_schedule[iteration] mom = momentum_schedule[iteration] teacher_temp = teacher_temp_schedule[iteration] last_layer_lr = last_layer_lr_schedule[iteration] apply_optim_scheduler(optimizer, lr, wd, last_layer_lr) # compute losses optimizer.zero_grad(set_to_none=True) loss_dict = model.forward_backward(data, teacher_temp=teacher_temp) # clip gradients if fp16_scaler is not None: if cfg.optim.clip_grad: fp16_scaler.unscale_(optimizer) for v in model.student.values(): v.clip_grad_norm_(cfg.optim.clip_grad) fp16_scaler.step(optimizer) fp16_scaler.update() else: if cfg.optim.clip_grad: for v in model.student.values(): v.clip_grad_norm_(cfg.optim.clip_grad) optimizer.step() # perform teacher EMA update model.update_teacher(mom) # logging if distributed.get_global_size() > 1: for v in loss_dict.values(): torch.distributed.all_reduce(v) loss_dict_reduced = {k: v.item() / distributed.get_global_size() for k, v in loss_dict.items()} if math.isnan(sum(loss_dict_reduced.values())): logger.info("NaN detected") raise AssertionError losses_reduced = sum(loss for loss in loss_dict_reduced.values()) metric_logger.update(lr=lr) metric_logger.update(wd=wd) metric_logger.update(mom=mom) metric_logger.update(last_layer_lr=last_layer_lr) metric_logger.update(current_batch_size=current_batch_size) metric_logger.update(total_loss=losses_reduced, **loss_dict_reduced) # checkpointing and testing if cfg.evaluation.eval_period_iterations > 0 and (iteration + 1) % cfg.evaluation.eval_period_iterations == 0: do_test(cfg, model, f"training_{iteration}") torch.cuda.synchronize() periodic_checkpointer.step(iteration) iteration = iteration + 1 metric_logger.synchronize_between_processes() return {k: meter.global_avg for k, meter in metric_logger.meters.items()} def main(args): cfg = setup(args) model = SSLMetaArch(cfg).to(torch.device("cuda")) model.prepare_for_distributed_training() logger.info("Model:\n{}".format(model)) if args.eval_only: iteration = ( FSDPCheckpointer(model, save_dir=cfg.train.output_dir) .resume_or_load(cfg.MODEL.WEIGHTS, resume=not args.no_resume) .get("iteration", -1) + 1 ) return do_test(cfg, model, f"manual_{iteration}") do_train(cfg, model, resume=not args.no_resume) if __name__ == "__main__": args = get_args_parser(add_help=True).parse_args() main(args) ================================================ FILE: dinov2/utils/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. ================================================ FILE: dinov2/utils/checkpoint.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the CC-by-NC licence, # found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree. from typing import Any from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer from torch import nn import dinov2.distributed as dist class PeriodicCheckpointerWithCleanup(PeriodicCheckpointer): @property def does_write(self) -> bool: """See https://github.com/facebookresearch/fvcore/blob/main/fvcore/common/checkpoint.py#L114""" return self.checkpointer.save_dir and self.checkpointer.save_to_disk def save_best(self, **kwargs: Any) -> None: """Same argument as `Checkpointer.save`, to save a model named like `model_best.pth`""" self.checkpointer.save(f"{self.file_prefix}_best", **kwargs) def has_checkpoint(self) -> bool: return self.checkpointer.has_checkpoint() def get_checkpoint_file(self) -> str: # returns "" if the file does not exist return self.checkpointer.get_checkpoint_file() def load(self, path: str, checkpointables=None) -> dict[str, Any]: return self.checkpointer.load(path=path, checkpointables=checkpointables) def step(self, iteration: int, **kwargs: Any) -> None: if not self.does_write: # step also removes files, so should be deactivated when object does not write return super().step(iteration=iteration, **kwargs) def resume_or_load(checkpointer: Checkpointer, path: str, *, resume: bool = True) -> dict[str, Any]: """ If `resume` is True, this method attempts to resume from the last checkpoint, if exists. Otherwise, load checkpoint from the given path. Similar to Checkpointer.resume_or_load in fvcore https://github.com/facebookresearch/fvcore/blob/main/fvcore/common/checkpoint.py#L208 but always reload checkpointables, in case we want to resume the training in a new job. """ if resume and checkpointer.has_checkpoint(): path = checkpointer.get_checkpoint_file() return checkpointer.load(path) def build_periodic_checkpointer( model: nn.Module, save_dir="", *, period: int, max_iter=None, max_to_keep=None, **checkpointables: Any, ) -> PeriodicCheckpointerWithCleanup: """Util to build a `PeriodicCheckpointerWithCleanup`.""" checkpointer = Checkpointer(model, save_dir, **checkpointables, save_to_disk=dist.is_main_process()) return PeriodicCheckpointerWithCleanup(checkpointer, period, max_iter=max_iter, max_to_keep=max_to_keep) ================================================ FILE: dinov2/utils/cluster.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from enum import Enum import os from pathlib import Path from typing import Any, Dict, Optional class ClusterType(Enum): AWS = "aws" FAIR = "fair" RSC = "rsc" def _guess_cluster_type() -> ClusterType: uname = os.uname() if uname.sysname == "Linux": if uname.release.endswith("-aws"): # Linux kernel versions on AWS instances are of the form "5.4.0-1051-aws" return ClusterType.AWS elif uname.nodename.startswith("rsc"): # Linux kernel versions on RSC instances are standard ones but hostnames start with "rsc" return ClusterType.RSC return ClusterType.FAIR def get_cluster_type(cluster_type: Optional[ClusterType] = None) -> Optional[ClusterType]: if cluster_type is None: return _guess_cluster_type() return cluster_type def get_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]: cluster_type = get_cluster_type(cluster_type) if cluster_type is None: return None CHECKPOINT_DIRNAMES = { ClusterType.AWS: "checkpoints", ClusterType.FAIR: "checkpoint", ClusterType.RSC: "checkpoint/dino", } return Path("/") / CHECKPOINT_DIRNAMES[cluster_type] def get_user_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]: checkpoint_path = get_checkpoint_path(cluster_type) if checkpoint_path is None: return None username = os.environ.get("USER") assert username is not None return checkpoint_path / username def get_slurm_partition(cluster_type: Optional[ClusterType] = None) -> Optional[str]: cluster_type = get_cluster_type(cluster_type) if cluster_type is None: return None SLURM_PARTITIONS = { ClusterType.AWS: "learnaccel", ClusterType.FAIR: "learnaccel", ClusterType.RSC: "learn", } return SLURM_PARTITIONS[cluster_type] def get_slurm_executor_parameters( nodes: int, num_gpus_per_node: int, cluster_type: Optional[ClusterType] = None, **kwargs ) -> Dict[str, Any]: # create default parameters params = { "mem_gb": 0, # Requests all memory on a node, see https://slurm.schedmd.com/sbatch.html "gpus_per_node": num_gpus_per_node, "tasks_per_node": num_gpus_per_node, # one task per GPU "cpus_per_task": 10, "nodes": nodes, "slurm_partition": get_slurm_partition(cluster_type), } # apply cluster-specific adjustments cluster_type = get_cluster_type(cluster_type) if cluster_type == ClusterType.AWS: params["cpus_per_task"] = 12 del params["mem_gb"] elif cluster_type == ClusterType.RSC: params["cpus_per_task"] = 12 # set additional parameters / apply overrides params.update(kwargs) return params ================================================ FILE: dinov2/utils/config.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import math import logging import os from omegaconf import OmegaConf import dinov2.distributed as distributed from dinov2.logging import setup_logging from dinov2.utils import utils from dinov2.configs import dinov2_default_config logger = logging.getLogger("dinov2") def apply_scaling_rules_to_cfg(cfg): # to fix if cfg.optim.scaling_rule == "sqrt_wrt_1024": base_lr = cfg.optim.base_lr cfg.optim.lr = base_lr cfg.optim.lr *= math.sqrt(cfg.train.batch_size_per_gpu * distributed.get_global_size() / 1024.0) logger.info(f"sqrt scaling learning rate; base: {base_lr}, new: {cfg.optim.lr}") else: raise NotImplementedError return cfg def write_config(cfg, output_dir, name="config.yaml"): logger.info(OmegaConf.to_yaml(cfg)) saved_cfg_path = os.path.join(output_dir, name) with open(saved_cfg_path, "w") as f: OmegaConf.save(config=cfg, f=f) return saved_cfg_path def get_cfg_from_args(args): args.output_dir = os.path.abspath(args.output_dir) args.opts += [f"train.output_dir={args.output_dir}"] default_cfg = OmegaConf.create(dinov2_default_config) cfg = OmegaConf.load(args.config_file) cfg = OmegaConf.merge(default_cfg, cfg, OmegaConf.from_cli(args.opts)) return cfg def default_setup(args): distributed.enable(overwrite=True) seed = getattr(args, "seed", 0) rank = distributed.get_global_rank() global logger setup_logging(output=args.output_dir, level=logging.INFO) logger = logging.getLogger("dinov2") utils.fix_random_seeds(seed + rank) logger.info("git:\n {}\n".format(utils.get_sha())) logger.info("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items()))) def setup(args): """ Create configs and perform basic setups. """ cfg = get_cfg_from_args(args) os.makedirs(args.output_dir, exist_ok=True) default_setup(args) apply_scaling_rules_to_cfg(cfg) write_config(cfg, args.output_dir) return cfg ================================================ FILE: dinov2/utils/dtype.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from typing import Dict, Union import numpy as np import torch TypeSpec = Union[str, np.dtype, torch.dtype] _NUMPY_TO_TORCH_DTYPE: Dict[np.dtype, torch.dtype] = { np.dtype("bool"): torch.bool, np.dtype("uint8"): torch.uint8, np.dtype("int8"): torch.int8, np.dtype("int16"): torch.int16, np.dtype("int32"): torch.int32, np.dtype("int64"): torch.int64, np.dtype("float16"): torch.float16, np.dtype("float32"): torch.float32, np.dtype("float64"): torch.float64, np.dtype("complex64"): torch.complex64, np.dtype("complex128"): torch.complex128, } def as_torch_dtype(dtype: TypeSpec) -> torch.dtype: if isinstance(dtype, torch.dtype): return dtype if isinstance(dtype, str): dtype = np.dtype(dtype) assert isinstance(dtype, np.dtype), f"Expected an instance of nunpy dtype, got {type(dtype)}" return _NUMPY_TO_TORCH_DTYPE[dtype] ================================================ FILE: dinov2/utils/param_groups.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from collections import defaultdict import logging logger = logging.getLogger("dinov2") def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12, force_is_backbone=False, chunked_blocks=False): """ Calculate lr decay rate for different ViT blocks. Args: name (string): parameter name. lr_decay_rate (float): base lr decay rate. num_layers (int): number of ViT blocks. Returns: lr decay rate for the given parameter. """ layer_id = num_layers + 1 if name.startswith("backbone") or force_is_backbone: if ( ".pos_embed" in name or ".patch_embed" in name or ".mask_token" in name or ".cls_token" in name or ".register_tokens" in name ): layer_id = 0 elif force_is_backbone and ( "pos_embed" in name or "patch_embed" in name or "mask_token" in name or "cls_token" in name or "register_tokens" in name ): layer_id = 0 elif ".blocks." in name and ".residual." not in name: layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1 elif chunked_blocks and "blocks." in name and "residual." not in name: layer_id = int(name[name.find("blocks.") :].split(".")[2]) + 1 elif "blocks." in name and "residual." not in name: layer_id = int(name[name.find("blocks.") :].split(".")[1]) + 1 return lr_decay_rate ** (num_layers + 1 - layer_id) def get_params_groups_with_decay(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0): chunked_blocks = False if hasattr(model, "n_blocks"): logger.info("chunked fsdp") n_blocks = model.n_blocks chunked_blocks = model.chunked_blocks elif hasattr(model, "blocks"): logger.info("first code branch") n_blocks = len(model.blocks) elif hasattr(model, "backbone"): logger.info("second code branch") n_blocks = len(model.backbone.blocks) else: logger.info("else code branch") n_blocks = 0 all_param_groups = [] for name, param in model.named_parameters(): name = name.replace("_fsdp_wrapped_module.", "") if not param.requires_grad: continue decay_rate = get_vit_lr_decay_rate( name, lr_decay_rate, num_layers=n_blocks, force_is_backbone=n_blocks > 0, chunked_blocks=chunked_blocks ) d = {"params": param, "is_last_layer": False, "lr_multiplier": decay_rate, "wd_multiplier": 1.0, "name": name} if "last_layer" in name: d.update({"is_last_layer": True}) if name.endswith(".bias") or "norm" in name or "gamma" in name: d.update({"wd_multiplier": 0.0}) if "patch_embed" in name: d.update({"lr_multiplier": d["lr_multiplier"] * patch_embed_lr_mult}) all_param_groups.append(d) logger.info(f"""{name}: lr_multiplier: {d["lr_multiplier"]}, wd_multiplier: {d["wd_multiplier"]}""") return all_param_groups def fuse_params_groups(all_params_groups, keys=("lr_multiplier", "wd_multiplier", "is_last_layer")): fused_params_groups = defaultdict(lambda: {"params": []}) for d in all_params_groups: identifier = "" for k in keys: identifier += k + str(d[k]) + "_" for k in keys: fused_params_groups[identifier][k] = d[k] fused_params_groups[identifier]["params"].append(d["params"]) return fused_params_groups.values() ================================================ FILE: dinov2/utils/utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. import logging import os import random import subprocess from urllib.parse import urlparse import numpy as np import torch from torch import nn logger = logging.getLogger("dinov2") def load_pretrained_weights(model, pretrained_weights, checkpoint_key): if urlparse(pretrained_weights).scheme: # If it looks like an URL state_dict = torch.hub.load_state_dict_from_url(pretrained_weights, map_location="cpu") else: state_dict = torch.load(pretrained_weights, map_location="cpu") if checkpoint_key is not None and checkpoint_key in state_dict: logger.info(f"Take key {checkpoint_key} in provided checkpoint dict") state_dict = state_dict[checkpoint_key] # remove `module.` prefix state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} # remove `backbone.` prefix induced by multicrop wrapper state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()} msg = model.load_state_dict(state_dict, strict=False) logger.info("Pretrained weights found at {} and loaded with msg: {}".format(pretrained_weights, msg)) def fix_random_seeds(seed=31): """ Fix random seeds. """ torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) def get_sha(): cwd = os.path.dirname(os.path.abspath(__file__)) def _run(command): return subprocess.check_output(command, cwd=cwd).decode("ascii").strip() sha = "N/A" diff = "clean" branch = "N/A" try: sha = _run(["git", "rev-parse", "HEAD"]) subprocess.check_output(["git", "diff"], cwd=cwd) diff = _run(["git", "diff-index", "HEAD"]) diff = "has uncommitted changes" if diff else "clean" branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"]) except Exception: pass message = f"sha: {sha}, status: {diff}, branch: {branch}" return message class CosineScheduler(object): def __init__(self, base_value, final_value, total_iters, warmup_iters=0, start_warmup_value=0, freeze_iters=0): super().__init__() self.final_value = final_value self.total_iters = total_iters freeze_schedule = np.zeros((freeze_iters)) warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) iters = np.arange(total_iters - warmup_iters - freeze_iters) schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters))) self.schedule = np.concatenate((freeze_schedule, warmup_schedule, schedule)) assert len(self.schedule) == self.total_iters def __getitem__(self, it): if it >= self.total_iters: return self.final_value else: return self.schedule[it] def has_batchnorms(model): bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm) for name, module in model.named_modules(): if isinstance(module, bn_types): return True return False ================================================ FILE: docs/README_CELL_DINO.md ================================================ # Cell-DINO: Self-Supervised Image-based Embeddings for Cell Fluorescent Microscopy Théo Moutakanni*, Camille Couprie*, Seungeun Yi*, Elouan Gardes*, Piotr Bojanowski*, Hugo Touvron*, Michael Doron, Zitong S. Chen, Nikita Moshkov, Mathilde Caron, Armand Joulin, Wolfgang M. Pernice, Juan C. Caicedo [[`BibTeX`](#citing-cell-dino)] **[*Meta AI Research, FAIR](https://ai.facebook.com/research/)** PyTorch implementation and pretrained models for Cell-DINO. The contents of this repo, including the code and model weights, are intended for research use only. It is not for use in medical procedures, including any diagnostics, treatment, or curative applications. Do not use this model for any clinical purpose or as a substitute for professional medical judgement. ![teaser](Cell-DINO.png) ## Pretrained models One model pretrained on HPA single cell, one model pretrained on HPA Field of View, one model pretrained on HPA Field of View at a larger resolution and one model pretrained on the combined cell painting dataset are released, see dowload instructions at the end of README.md, section "DINO for Biology". ## Installation Follow instructions in the DINOv2 README or build the following environment: ```shell conda create -n py310 python=3.10 conda activate py310 pip install -r requirements.txt pip install -U scikit-learn ``` ## Data preparation The HPA-FoV and HPA single cell (HPAone) datasets are available [here](https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2443) The folder also contains three sample images ("sample_images") that can be dowloaded to test quickly inference in the provided notebook in notebooks/cell_dino THe required files for HPA single cell (dataloader : HPAone.py) are: fixed_size_masked_single_cells_HPA varied_size_masked_single_cells_HPA varied_size_masked_single_cells_pretrain_20240507.csv fixed_size_masked_single_cells_evaluation_20240507.csv fixed_size_masked_single_cells_pretrain_20240507.csv :warning: To execute the commands provided in the next sections for training and evaluation, the `dinov2` package should be included in the Python module search path, i.e. simply prefix the command to run with `PYTHONPATH=.`. ## Training ### Fast setup: training DINOv2 ViT-L/16 on HPA single cell dataset Run CellDINO training on 4 A100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit: ```shell python dinov2/run/train/train.py \ --nodes 4 \ --config-file dinov2/configs/train/cell_dino/vitl16_hpaone.yaml \ --output-dir \ train.dataset_path=HPAone:split=ALL:root= ``` Training time is approximately 2 days on 4 A100 GPU nodes and the resulting checkpoint should reach 78.5 F1 accuracy for protein localization with a linear evaluation. The training code saves the weights of the teacher in the `eval` folder every 9000 iterations for evaluation. ## Evaluation The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node: ### Linear classification with data augmentation on HPAone or HPAFov (replace 'HPAone' by 'HPAFoV'): ```shell PYTHONPATH=.:dinov2/data python dinov2/run/eval/cell_dino/linear.py \ --config-file dinov2/configs/eval/cell_dino/vitl16_pretrain.yaml \ --pretrained-weights /eval/training_44999/teacher_checkpoint.pth \ --output-dir /eval/training_44999/linear \ --train-dataset HPAone:split=TRAIN:mode=PROTEIN_LOCALIZATION:root= \ --val-dataset HPAone:split=VAL:mode=PROTEIN_LOCALIZATION:root \ --val-metric-type mean_per_class_multilabel_f1 \ --loss-type binary_cross_entropy \ --avgpool \ ``` We release the weights from evaluating the different models: The performance of the provided pretrained model weights can be evaluated as follows on HPAone and HPAFoV (replace 'HPAone' by 'HPAFoV', and make sure to load the correct model weights) for the protein localization task: ```shell PYTHONPATH=.:dinov2/data python dinov2/run/eval/linear_celldino.py \ --config-file dinov2/configs/eval/cell_dino/vitl16_pretrain.yaml \ --pretrained-weights \ --output-dir \ --train-dataset HPAone:split=TRAIN:mode=PROTEIN_LOCALIZATION:root= \ --val-dataset HPAone:split=VAL:mode=PROTEIN_LOCALIZATION:root=root \ --val-metric-type mean_per_class_multilabel_f1 \ --loss-type binary_cross_entropy \ --avgpool \ ``` and ```shell PYTHONPATH=.:dinov2/data python dinov2/run/eval/cell_dino/linear.py \ --config-file dinov2/configs/eval/cell_dino/vitl16_pretrain.yaml \ --pretrained-weights \ --output-dir \ --train-dataset HPAone:split=TRAIN:mode=CELL_TYPE:root= \ --val-dataset HPAone:split=VAL:mode=CELL_TYPE:root=root \ --val-metric-type mean_per_class_multiclass_f1 \ --avgpool \ ``` for the cell line classification task. ### knn evaluation on HPAone: ```shell PYTHONPATH=.:dinov2/data python dinov2/run/eval/cell_dino/knn.py \ --config-file dinov2/configs/eval/cell_dino/vitl16_pretrain.yaml \ --pretrained-weights \ --output-dir \ --train-dataset HPAone:split=TRAIN:mode=CELL_TYPE:root= \ --val-dataset HPAone:split=VAL:mode=CELL_TYPE:root= \ --metric-type mean_per_class_multiclass_f1 \ --crop-size 384 \ --batch-size 256 \ --resize-size 0 \ --nb_knn 1 \ ``` for the cell line classification task. For the knn evaluation on HPAFoV, replace 'HPAone' by 'HPAFoV' in the command above. For protein localization, don't forget to change the metric type to mean_per_class_multilabel_f1. ## License Cell-DINO code is released under the CC by NC licence See [LICENSE_CELL_DINO_CODE](LICENSE_CELL_DINO_CODE) for additional details. Model weights will be released under the FAIR Non-Commercial Research License. See [LICENSE_CELL_DINO_MODELS](LICENSE_CELL_DINO_MODELS) for additional details. ## Contributing See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md). ## Citing Cell-DINO If you find this repository useful, please consider giving a star :star: and citation :t-rex:: ``` @misc{, title={Cell-DINO: Self-Supervised Image-based Embeddings for Cell Fluorescent Microscopy}, author={Moutakanni, Th\'eo and Couprie, Camille and Yi, Seungeun and Gardes, Elouan Gardes and Bojanowski, Piotr and Touvron, Hugo and Doron, Michael and Chen, Zitong S. and Moshkov, Nikita and Caron, Mathilde and Joulin, Armand and Pernice, Wolfgang M. and Caicedo, Juan C.}, journal={in review to PloS One on Computational Biology}, year={2025} } ``` ================================================ FILE: docs/README_CHANNEL_ADAPTIVE_DINO.md ================================================ # Scaling Channel-Adaptive Self-Supervised Learning [[`Paper `](https://openreview.net/forum?id=pT8sgtRVAf))] [[`BibTeX`](#citing-channeladaptivedino-and-dinov2)] **[Meta AI Research, FAIR](https://ai.facebook.com/research/)** Alice V. De Lorenci, Seungeun Yi, Théo Moutakanni, Piotr Bojanowski, Camille Couprie, Juan C. Caicedo, Wolfgang M. Pernice, with special thanks to Elouan Gardes for his contributions to the codebase. PyTorch implementation and pretrained model for ChannelAdaptive-DINO. The contents of this repo, including the code and model weights, are intended for research use only. It is not for use in medical procedures, including any diagnostics, treatment, or curative applications. Do not use this model for any clinical purpose or as a substitute for professional medical judgement. ![teaser](ChannelAdaptiveDINO.png) ## Pretrained model You can download the model weights trained on the Extended CHAMMI dataset (combination of five cell microscopy datasets with variable numbers of channels) on torchhub. ## Installation Follow instructions in the DINOv2 README. There are two additionnal dependencies to pandas and tifffile. ## What is included / not included This repository includes the Bag of Channel implementation, not the Hierarchical attention approach. ## Data preparation The CHAMMI dataset is available [here](https://github.com/chaudatascience/channel_adaptive_models). The HPA-FoV dataset is available [here](https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2443) Content: a directory new_512_whole_images and two csv files: "whole_images_512_test.csv" "whole_images_512_train.csv" :warning: To execute the commands provided in the next sections for training and evaluation, the `dinov2` package should be included in the Python module search path, i.e. simply prefix the command to run with `PYTHONPATH=.`. ## Training ### Fast setup: training Channel-Adaptive DINO ViT-L/16 on HPA single cell dataset Run Channel-Adaptive DINO training on 4 A100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit: ```shell python dinov2/run/train/train.py \ --nodes 4 \ --config-file dinov2/configs/train/cell_dino/vitl16_boc_hpafov.yaml \ --output-dir \ train.dataset_path=HPAFoV:split=TRAIN:root=:wildcard=SEPARATE_CHANNELS" ``` Training time is approximately 2 days. The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation. This example only performs pretraining on the HPA-FoV dataset. ## Evaluation The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node: ### Linear Evaluation on HPAFoV ```shell PYTHONPATH=.:dinov2/data python dinov2/run/eval/cell_dino/linear.py \ --config-file dinov2/configs/eval/cell_dino/vitl16_channel_adaptive_pretrain.yaml \ --pretrained-weights /eval/training_359999/teacher_checkpoint.pth \ --output-dir /eval/training_359999/linear \ --train-dataset HPAFoV:split=TRAIN:mode=PROTEIN_LOCALIZATION:root= \ --val-dataset HPAFoV:split=VAL:mode=PROTEIN_LOCALIZATION:root= \ --val-metric-type mean_per_class_multilabel_f1 \ --loss-type binary_cross_entropy \ --bag-of-channels \ --crop-size 384 \ --n-last-blocks 4 \ --batch-size 32 \ --epoch-length 145 \ --epochs 30 \ --avgpool \ ``` ### KNN classification on CHAMMI Go to the docs directory, modifify some paths in launcher_knn_eval_on_chammi.sh and run ```shell ./launcher_knn_eval_on_chammi.sh WTC TASK_ONE ; ./launcher_knn_eval_on_chammi.sh WTC TASK_TWO ; ./launcher_knn_eval_on_chammi.sh HPA TASK_ONE ; ./launcher_knn_eval_on_chammi.sh HPA TASK_TWO ; ./launcher_knn_eval_on_chammi.sh HPA TASK_THREE ; ./launcher_knn_eval_on_chammi.sh CP TASK_ONE ; ./launcher_knn_eval_on_chammi.sh CP TASK_TWO ; ./launcher_knn_eval_on_chammi.sh CP TASK_THREE ; ./launcher_knn_eval_on_chammi.sh CP TASK_FOUR ; ``` ### Linear classification on CHAMMI Go to the docs directory, modifify some paths in launcher_CHAMMI_eval.sh and run ```shell ./launcher_CHAMMI_eval.sh WTC TASK_ONE ; ./launcher_CHAMMI_eval.sh WTC TASK_TWO ; ./launcher_CHAMMI_eval.sh HPA TASK_ONE ; ./launcher_CHAMMI_eval.sh HPA TASK_TWO ; ./launcher_CHAMMI_eval.sh HPA TASK_THREE ; ./launcher_CHAMMI_eval.sh CP TASK_ONE ; ./launcher_CHAMMI_eval.sh CP TASK_TWO ; ./launcher_CHAMMI_eval.sh CP TASK_THREE ; ./launcher_CHAMMI_eval.sh CP TASK_FOUR ; ``` | | WTC - Task 1 | WTC - Task 2 | HPA - Task 1 | HPA - Task 2 | HPA - Task 3 | CP - Task 1 | CP - Task 2 | CP - Task 3 | CP - Task 4 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | knn reproduced | 80.3 | 79.3 | 91.6 | 61.4 | 29.0 | 89.8 | 57.6 | 23.4 | 18.4 | | knn paper | 79.4 | 79.0 | 86.6 | 59.3 | 29.6 | 92.6 | 57.6 | 22.1 | 18.5 | | Linear reproduced | 89.9 | 87.9 | 92.7 | 87.2 | 66.2 | 89.9 | 59.8 | 26.6 | 32.5| | Linear paper | 90.5 | 89.2 | 88.3 | 84.7 | 65.0 | 90.5 | 60.5 | 25.8 | 32.7| ## License Cell-DINO code is released under the CC by NC licence See [LICENSE_CELL_DINO_CODE](LICENSE_CELL_DINO_CODE) for additional details. Model weights will be released under the FAIR Non-Commercial Research License. See [LICENSE_CELL_DINO_MODELS](LICENSE_CELL_DINO_MODELS) for additional details. ## Contributing See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md). ## Citing ChannelAdaptiveDINO and DINOv2 If you find this repository useful, please consider giving a star :star: and citation :t-rex:: ``` @misc{Delorenci2025scaling, title={Scaling Channel-Adaptive Self-Supervised Learning}, author={V. De Lorenci, Alice and Yi, Seungeun and Moutakanni, Theo and Bojanowski, Piotr and Couprie, Camille and Caicedo, Juan C. and Pernice, Wolfgang M.}, journal={TMLR}, year={2025} } ``` ``` @misc{oquab2023dinov2, title={DINOv2: Learning Robust Visual Features without Supervision}, author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr}, journal={arXiv:2304.07193}, year={2023} } ``` ================================================ FILE: hubconf.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from dinov2.hub.backbones import dinov2_vitb14, dinov2_vitg14, dinov2_vitl14, dinov2_vits14 from dinov2.hub.backbones import dinov2_vitb14_reg, dinov2_vitg14_reg, dinov2_vitl14_reg, dinov2_vits14_reg from dinov2.hub.cell_dino.backbones import cell_dino_cp_vits8, cell_dino_hpa_vitl16, cell_dino_hpa_vitl14, channel_adaptive_dino_vitl16 from dinov2.hub.xray_dino.backbones import xray_dino_vitl16 from dinov2.hub.classifiers import dinov2_vitb14_lc, dinov2_vitg14_lc, dinov2_vitl14_lc, dinov2_vits14_lc from dinov2.hub.classifiers import dinov2_vitb14_reg_lc, dinov2_vitg14_reg_lc, dinov2_vitl14_reg_lc, dinov2_vits14_reg_lc from dinov2.hub.depthers import dinov2_vitb14_ld, dinov2_vitg14_ld, dinov2_vitl14_ld, dinov2_vits14_ld from dinov2.hub.depthers import dinov2_vitb14_dd, dinov2_vitg14_dd, dinov2_vitl14_dd, dinov2_vits14_dd from dinov2.hub.dinotxt import dinov2_vitl14_reg4_dinotxt_tet1280d20h24l dependencies = ["torch"] ================================================ FILE: notebooks/cell_dino/inference.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "id": "6c3f1fe9-af40-4a57-aff0-99313d722f34", "metadata": {}, "outputs": [], "source": [ "# Copyright (c) Meta Platforms, Inc. and affiliates.\n", "# This source code is licensed under the CC-by-NC licence,\n", "# found in the LICENSE_CELL_DINO_CODE file in the root directory of this source tree." ] }, { "cell_type": "code", "execution_count": null, "id": "bfe8d7c4-995c-44b4-a8d1-97be2badce8c", "metadata": {}, "outputs": [], "source": [ "SAMPLE_IMAGES_DIR = \"sample_images/\" # path to directory with cell images.\n", "REPO_DIR=\"\" # path to the dinov2 repo.\n", "# Also define the urls of the pretrained models CPurl, SCurl, FOVurl used in the next cell. Instructions to get the models urls are in the README.md." ] }, { "cell_type": "code", "execution_count": null, "id": "70b2a71b-8fb2-4ec3-8cd1-21d6f770f704", "metadata": {}, "outputs": [], "source": [ "import torch\n", "cell_dino_vits8 = torch.hub.load(REPO_DIR, 'cell_dino_cp_vits8', source='local', pretrained_url=CPurl)\n", "cell_dino_vitl16_sc = torch.hub.load(REPO_DIR, 'cell_dino_hpa_vitl16', source='local', pretrained_url=SCurl)\n", "cell_dino_vitl16_fov = torch.hub.load(REPO_DIR, 'cell_dino_hpa_vitl16', source='local', pretrained_url=FOVurl)\n", "#channel_adaptive_dino_vitl16 = torch.hub.load(REPO_DIR, 'channel_adaptive_dino_vitl16', source='local', pretrained_url=CAurl)\n", "# cell_dino_vitl14 = torch.hub.load(REPO_DIR, 'cell_dino_hpa_vitl14', source='local', pretrained_url=HRurl)" ] }, { "cell_type": "code", "execution_count": null, "id": "a6899c21-c4c2-43d4-8cdc-f6cce2c3686b", "metadata": {}, "outputs": [], "source": [ "import torch\n", "import torchvision\n", "from dinov2.hub.cell_dino.backbones import cell_dino_hpa_vitl16, cell_dino_cp_vits8\n", "from functools import partial\n", "from dinov2.eval.utils import ModelWithIntermediateLayers\n", "\n", "DEVICE = \"cuda:0\"\n", "\n", "class self_normalize(object):\n", " def __call__(self, x):\n", " x = x / 255\n", " m = x.mean((-2, -1), keepdim=True)\n", " s = x.std((-2, -1), unbiased=False, keepdim=True)\n", " x -= m\n", " x /= s + 1e-7\n", " return x\n", "\n", "normalize = self_normalize()" ] }, { "cell_type": "code", "execution_count": null, "id": "779ecbb7-3247-4b9e-9248-25c8cbd74d24", "metadata": {}, "outputs": [], "source": [ "# ---------------------- Example inference on HPA-FoV dataset --------------------------\n", "\n", "# 1- Read one human protein atlas HPA-FoV image (4 channels)\n", "img = torchvision.io.read_image(SAMPLE_IMAGES_DIR + \"HPA_FoV_00070df0-bbc3-11e8-b2bc-ac1f6b6435d0.png\")\n", "\n", "# 2- Normalise image as it was done for training\n", "img_hpa_fov = img.unsqueeze(0).to(device=DEVICE)\n", "img_hpa_fov = normalize(img_hpa_fov)\n", "\n", "# 3- Load model\n", "cell_dino_model = cell_dino_vitl16_fov\n", "cell_dino_model.to(device=DEVICE)\n", "cell_dino_model.eval()\n", "\n", "# 4- Inference\n", "features = cell_dino_model(img_hpa_fov)\n", "print(features)\n", "\n", "# 5- [Optional] feature extractor as used for linear evaluation\n", "autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=torch.float)\n", "model_with_interm_layers = ModelWithIntermediateLayers(cell_dino_model, 4, autocast_ctx)\n", "features_with_interm_layers = model_with_interm_layers(img_hpa_fov)" ] }, { "cell_type": "code", "execution_count": null, "id": "85e67e67-5824-4135-8ca2-f8396cf95cb2", "metadata": {}, "outputs": [], "source": [ "# ---------------------- Example inference on cell painting data --------------------------\n", "\n", "# 1- Read one cell painting image (5 channels)\n", "img = torchvision.io.read_image(SAMPLE_IMAGES_DIR + \"CP_BBBC036_24277_a06_1_976@140x149.png\")\n", "img5_channels = torch.zeros([1, 5, 160, 160])\n", "for c in range(5):\n", " img5_channels[0, c] = img[0, :, 160 * c : 160 * (c + 1)]\n", "img5_channels = img5_channels.to(device=DEVICE)\n", "\n", "# 2- Normalise image as it was done for training\n", "img5_channels = normalize(img5_channels)\n", "\n", "# 3- Load model\n", "cell_dino_model = cell_dino_vits8\n", "cell_dino_model.to(device=DEVICE)\n", "cell_dino_model.eval()\n", "\n", "# 4- Inference\n", "features = cell_dino_model(img5_channels)\n", "print(features[0,0:10])" ] }, { "cell_type": "code", "execution_count": null, "id": "ada26fcc-fd27-4dbf-983c-bbe06fe04b6f", "metadata": {}, "outputs": [], "source": [ "# ---------------------- Example inference on HPA single cell dataset --------------------------\n", "\n", "# Read one human protein atlas HPA single cell image (4 channels)\n", "img = torchvision.io.read_image(SAMPLE_IMAGES_DIR + \"HPA_single_cell_00285ce4-bba0-11e8-b2b9-ac1f6b6435d0_15.png\")\n", "\n", "# 2- Normalise image as it was done for training\n", "img_hpa = img.unsqueeze(0).to(device=DEVICE)\n", "img_hpa = normalize(img_hpa)\n", "\n", "# 3- Load model\n", "cell_dino_model = cell_dino_vitl16_sc\n", "cell_dino_model.to(device=DEVICE)\n", "cell_dino_model.eval()\n", "\n", "# 4- Inference\n", "features = cell_dino_model(img_hpa)\n", "print(features)\n", "\n", "torch.save(features.cpu(), \"sample_features_hpa.pt\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python (mypy310env)", "language": "python", "name": "mypy310env" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.19" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/depth_estimation.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Copyright (c) Meta Platforms, Inc. and affiliates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Depth Estimation \"Open" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "INSTALL = False # Switch this to install dependencies\n", "if INSTALL: # Try installing package with extras\n", " REPO_URL = \"https://github.com/facebookresearch/dinov2\"\n", " !{sys.executable} -m pip install -e {REPO_URL}'[extras]' --extra-index-url https://download.pytorch.org/whl/cu117 --extra-index-url https://pypi.nvidia.com\n", "else:\n", " REPO_PATH = \"\" # Specify a local path to the repository (or use installed package instead)\n", " sys.path.append(REPO_PATH)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utilities" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import math\n", "import itertools\n", "from functools import partial\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "\n", "from dinov2.eval.depth.models import build_depther\n", "\n", "\n", "class CenterPadding(torch.nn.Module):\n", " def __init__(self, multiple):\n", " super().__init__()\n", " self.multiple = multiple\n", "\n", " def _get_pad(self, size):\n", " new_size = math.ceil(size / self.multiple) * self.multiple\n", " pad_size = new_size - size\n", " pad_size_left = pad_size // 2\n", " pad_size_right = pad_size - pad_size_left\n", " return pad_size_left, pad_size_right\n", "\n", " @torch.inference_mode()\n", " def forward(self, x):\n", " pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))\n", " output = F.pad(x, pads)\n", " return output\n", "\n", "\n", "def create_depther(cfg, backbone_model, backbone_size, head_type):\n", " train_cfg = cfg.get(\"train_cfg\")\n", " test_cfg = cfg.get(\"test_cfg\")\n", " depther = build_depther(cfg.model, train_cfg=train_cfg, test_cfg=test_cfg)\n", "\n", " depther.backbone.forward = partial(\n", " backbone_model.get_intermediate_layers,\n", " n=cfg.model.backbone.out_indices,\n", " reshape=True,\n", " return_class_token=cfg.model.backbone.output_cls_token,\n", " norm=cfg.model.backbone.final_norm,\n", " )\n", "\n", " if hasattr(backbone_model, \"patch_size\"):\n", " depther.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone_model.patch_size)(x[0]))\n", "\n", " return depther" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load pretrained backbone" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using cache found in /private/home/plabatut/.cache/torch/hub/facebookresearch_dinov2_main\n" ] }, { "data": { "text/plain": [ "DinoVisionTransformer(\n", " (patch_embed): PatchEmbed(\n", " (proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14))\n", " (norm): Identity()\n", " )\n", " (blocks): ModuleList(\n", " (0-11): 12 x NestedTensorBlock(\n", " (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", " (attn): MemEffAttention(\n", " (qkv): Linear(in_features=384, out_features=1152, bias=True)\n", " (attn_drop): Dropout(p=0.0, inplace=False)\n", " (proj): Linear(in_features=384, out_features=384, bias=True)\n", " (proj_drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (ls1): LayerScale()\n", " (drop_path1): Identity()\n", " (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", " (mlp): Mlp(\n", " (fc1): Linear(in_features=384, out_features=1536, bias=True)\n", " (act): GELU(approximate='none')\n", " (fc2): Linear(in_features=1536, out_features=384, bias=True)\n", " (drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (ls2): LayerScale()\n", " (drop_path2): Identity()\n", " )\n", " )\n", " (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", " (head): Identity()\n", ")" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BACKBONE_SIZE = \"small\" # in (\"small\", \"base\", \"large\" or \"giant\")\n", "\n", "\n", "backbone_archs = {\n", " \"small\": \"vits14\",\n", " \"base\": \"vitb14\",\n", " \"large\": \"vitl14\",\n", " \"giant\": \"vitg14\",\n", "}\n", "backbone_arch = backbone_archs[BACKBONE_SIZE]\n", "backbone_name = f\"dinov2_{backbone_arch}\"\n", "\n", "backbone_model = torch.hub.load(repo_or_dir=\"facebookresearch/dinov2\", model=backbone_name)\n", "backbone_model.eval()\n", "backbone_model.cuda()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load pretrained depth head" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "load checkpoint from http path: https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_nyu_dpt_head.pth\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Downloading: \"https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_nyu_dpt_head.pth\" to /private/home/plabatut/.cache/torch/hub/checkpoints/dinov2_vits14_nyu_dpt_head.pth\n", "100%|██████████| 160M/160M [00:06<00:00, 27.2MB/s] \n" ] }, { "data": { "text/plain": [ "DepthEncoderDecoder(\n", " (backbone): DinoVisionTransformer()\n", " (decode_head): DPTHead(\n", " align_corners=False\n", " (loss_decode): ModuleList(\n", " (0): SigLoss()\n", " (1): GradientLoss()\n", " )\n", " (conv_depth): HeadDepth(\n", " (head): Sequential(\n", " (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (1): Interpolate()\n", " (2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (3): ReLU()\n", " (4): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " )\n", " (relu): ReLU()\n", " (sigmoid): Sigmoid()\n", " (reassemble_blocks): ReassembleBlocks(\n", " (projects): ModuleList(\n", " (0): ConvModule(\n", " (conv): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (1): ConvModule(\n", " (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (2): ConvModule(\n", " (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (3): ConvModule(\n", " (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " )\n", " (resize_layers): ModuleList(\n", " (0): ConvTranspose2d(48, 48, kernel_size=(4, 4), stride=(4, 4))\n", " (1): ConvTranspose2d(96, 96, kernel_size=(2, 2), stride=(2, 2))\n", " (2): Identity()\n", " (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", " )\n", " (readout_projects): ModuleList(\n", " (0-3): 4 x Sequential(\n", " (0): Linear(in_features=768, out_features=384, bias=True)\n", " (1): GELU(approximate='none')\n", " )\n", " )\n", " )\n", " (convs): ModuleList(\n", " (0): ConvModule(\n", " (conv): Conv2d(48, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (1): ConvModule(\n", " (conv): Conv2d(96, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (2): ConvModule(\n", " (conv): Conv2d(192, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (3): ConvModule(\n", " (conv): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " )\n", " (fusion_blocks): ModuleList(\n", " (0): FeatureFusionBlock(\n", " (project): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (res_conv_unit1): None\n", " (res_conv_unit2): PreActResidualConvUnit(\n", " (conv1): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (activate): ReLU(inplace=True)\n", " )\n", " (conv2): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (activate): ReLU(inplace=True)\n", " )\n", " )\n", " )\n", " (1-3): 3 x FeatureFusionBlock(\n", " (project): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (res_conv_unit1): PreActResidualConvUnit(\n", " (conv1): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (activate): ReLU(inplace=True)\n", " )\n", " (conv2): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (activate): ReLU(inplace=True)\n", " )\n", " )\n", " (res_conv_unit2): PreActResidualConvUnit(\n", " (conv1): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (activate): ReLU(inplace=True)\n", " )\n", " (conv2): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (activate): ReLU(inplace=True)\n", " )\n", " )\n", " )\n", " )\n", " (project): ConvModule(\n", " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", " (activate): ReLU(inplace=True)\n", " )\n", " )\n", ")" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import urllib\n", "\n", "import mmcv\n", "from mmcv.runner import load_checkpoint\n", "\n", "\n", "def load_config_from_url(url: str) -> str:\n", " with urllib.request.urlopen(url) as f:\n", " return f.read().decode()\n", "\n", "\n", "HEAD_DATASET = \"nyu\" # in (\"nyu\", \"kitti\")\n", "HEAD_TYPE = \"dpt\" # in (\"linear\", \"linear4\", \"dpt\")\n", "\n", "\n", "DINOV2_BASE_URL = \"https://dl.fbaipublicfiles.com/dinov2\"\n", "head_config_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_config.py\"\n", "head_checkpoint_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_head.pth\"\n", "\n", "cfg_str = load_config_from_url(head_config_url)\n", "cfg = mmcv.Config.fromstring(cfg_str, file_format=\".py\")\n", "\n", "model = create_depther(\n", " cfg,\n", " backbone_model=backbone_model,\n", " backbone_size=BACKBONE_SIZE,\n", " head_type=HEAD_TYPE,\n", ")\n", "\n", "load_checkpoint(model, head_checkpoint_url, map_location=\"cpu\")\n", "model.eval()\n", "model.cuda()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load sample image" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import urllib\n", "\n", "from PIL import Image\n", "\n", "\n", "def load_image_from_url(url: str) -> Image:\n", " with urllib.request.urlopen(url) as f:\n", " return Image.open(f).convert(\"RGB\")\n", "\n", "\n", "EXAMPLE_IMAGE_URL = \"https://dl.fbaipublicfiles.com/dinov2/images/example.jpg\"\n", "\n", "\n", "image = load_image_from_url(EXAMPLE_IMAGE_URL)\n", "display(image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate depth on sample image" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "from torchvision import transforms\n", "\n", "\n", "def make_depth_transform() -> transforms.Compose:\n", " return transforms.Compose([\n", " transforms.ToTensor(),\n", " lambda x: 255.0 * x[:3], # Discard alpha component and scale by 255\n", " transforms.Normalize(\n", " mean=(123.675, 116.28, 103.53),\n", " std=(58.395, 57.12, 57.375),\n", " ),\n", " ])\n", "\n", "\n", "def render_depth(values, colormap_name=\"magma_r\") -> Image:\n", " min_value, max_value = values.min(), values.max()\n", " normalized_values = (values - min_value) / (max_value - min_value)\n", "\n", " colormap = matplotlib.colormaps[colormap_name]\n", " colors = colormap(normalized_values, bytes=True) # ((1)xhxwx4)\n", " colors = colors[:, :, :3] # Discard alpha component\n", " return Image.fromarray(colors)\n", "\n", "\n", "transform = make_depth_transform()\n", "\n", "scale_factor = 1\n", "rescaled_image = image.resize((scale_factor * image.width, scale_factor * image.height))\n", "transformed_image = transform(rescaled_image)\n", "batch = transformed_image.unsqueeze(0).cuda() # Make a batch of one image\n", "\n", "with torch.inference_mode():\n", " result = model.whole_inference(batch, img_meta=None, rescale=True)\n", "\n", "depth_image = render_depth(result.squeeze().cpu())\n", "display(depth_image)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.17" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: notebooks/dinotxt.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": null, "id": "f75733e2", "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "INSTALL = False # Switch this to install dependencies\n", "if INSTALL: # Try installing package with extras\n", " REPO_URL = \"https://github.com/facebookresearch/dinov2\"\n", " !{sys.executable} -m pip install -e {REPO_URL}'[extras]' --extra-index-url https://download.pytorch.org/whl/cu117 --extra-index-url https://pypi.nvidia.com\n", "else:\n", " REPO_PATH = \"\" # Specify a local path to the repository (or use installed package instead)\n", " sys.path.append(REPO_PATH)" ] }, { "cell_type": "markdown", "id": "ff529826", "metadata": {}, "source": [ "## Load pretrained dino.txt vision head and text model" ] }, { "cell_type": "code", "execution_count": null, "id": "d20749e6", "metadata": {}, "outputs": [], "source": [ "from dinov2.hub.dinotxt import dinov2_vitl14_reg4_dinotxt_tet1280d20h24l, get_tokenizer\n", "model = dinov2_vitl14_reg4_dinotxt_tet1280d20h24l().cuda()" ] }, { "cell_type": "code", "execution_count": null, "id": "09c04bb1", "metadata": {}, "outputs": [], "source": [ "tokenizer = get_tokenizer()" ] }, { "cell_type": "markdown", "id": "b85bdd35", "metadata": {}, "source": [ "## Load sample image" ] }, { "cell_type": "code", "execution_count": null, "id": "0d48a6cf", "metadata": {}, "outputs": [], "source": [ "import urllib\n", "from PIL import Image\n", "\n", "def load_image_from_url(url: str) -> Image:\n", " with urllib.request.urlopen(url) as f:\n", " return Image.open(f).convert(\"RGB\")\n", "\n", "\n", "EXAMPLE_IMAGE_URL = \"https://dl.fbaipublicfiles.com/dinov2/images/example.jpg\"\n", "img_pil = load_image_from_url(EXAMPLE_IMAGE_URL)\n", "display(img_pil)" ] }, { "cell_type": "markdown", "id": "127d7e9d", "metadata": {}, "source": [ "## Get Zero-shot classification scores on sample image" ] }, { "cell_type": "code", "execution_count": null, "id": "cf2aa337", "metadata": {}, "outputs": [], "source": [ "import torch\n", "from dinov2.data.transforms import make_classification_eval_transform\n", "\n", "image_preprocess = make_classification_eval_transform()\n", "image_tensor = torch.stack([image_preprocess(img_pil)], dim=0).cuda()\n", "texts = [\"photo of dogs\", \"photo of a chair\", \"photo of a bowl\", \"photo of a tupperware\"]\n", "class_names = [\"dog\", \"chair\", \"bowl\", \"tupperware\"]\n", "tokenized_texts_tensor = tokenizer.tokenize(texts).cuda()\n", "with torch.autocast('cuda', dtype=torch.float):\n", " with torch.no_grad():\n", " image_features = model.encode_image(image_tensor)\n", " text_features = model.encode_text(tokenized_texts_tensor)\n", "image_features /= image_features.norm(dim=-1, keepdim=True)\n", "text_features /= text_features.norm(dim=-1, keepdim=True)\n", "similarity = (\n", " text_features.cpu().float().numpy() @ image_features.cpu().float().numpy().T\n", ")\n", "print(similarity) " ] }, { "cell_type": "markdown", "id": "c96f5edc", "metadata": {}, "source": [ "## Get patch embeddings" ] }, { "cell_type": "code", "execution_count": null, "id": "098f7339", "metadata": {}, "outputs": [], "source": [ "with torch.autocast('cuda', dtype=torch.float):\n", " with torch.no_grad():\n", " image_class_tokens, image_patch_tokens = model.get_visual_class_and_patch_tokens(image_tensor)\n", " text_features_aligned_to_patch = model.encode_text(tokenized_texts_tensor)[:, 1024:] # Part of text features that is aligned to patch features" ] }, { "cell_type": "code", "execution_count": null, "id": "244138fb", "metadata": {}, "outputs": [], "source": [ "import torch.nn.functional as F\n", "\n", "B, P, D = image_patch_tokens.shape\n", "H = W = int(P**0.5) \n", "x = image_patch_tokens.movedim(2, 1).unflatten(2, (H, W)).float() # [B, D, H, W]\n", "x = F.interpolate(x, size=(480, 640), mode=\"bicubic\", align_corners=False)\n", "x = F.normalize(x, p=2, dim=1)\n", "y = F.normalize(text_features_aligned_to_patch.float(), p=2, dim=1)\n", "per_patch_similarity_to_text = torch.einsum(\"bdhw,cd->bchw\", x, y)\n", "pred_idx = per_patch_similarity_to_text.argmax(1).squeeze(0)" ] }, { "cell_type": "markdown", "id": "02b6c903", "metadata": {}, "source": [ "# Zero-shot classification on ImageNet1k " ] }, { "cell_type": "code", "execution_count": null, "id": "ddf76eb0", "metadata": {}, "outputs": [], "source": [ "# https://raw.githubusercontent.com/kantharajucn/CLIP-imagenet-evaluation/refs/heads/main/classes.py\n", "imagenet_clip_class_names= [\"tench\", \"goldfish\", \"great white shark\", \"tiger shark\", \"hammerhead shark\", \"electric ray\", \"stingray\", \"rooster\", \"hen\", \"ostrich\", \"brambling\", \"goldfinch\", \"house finch\", \"junco\", \"indigo bunting\", \"American robin\", \"bulbul\", \"jay\", \"magpie\", \"chickadee\", \"American dipper\", \"kite (bird of prey)\", \"bald eagle\", \"vulture\", \"great grey owl\", \"fire salamander\", \"smooth newt\", \"newt\", \"spotted salamander\", \"axolotl\", \"American bullfrog\", \"tree frog\", \"tailed frog\", \"loggerhead sea turtle\", \"leatherback sea turtle\", \"mud turtle\", \"terrapin\", \"box turtle\", \"banded gecko\", \"green iguana\", \"Carolina anole\", \"desert grassland whiptail lizard\", \"agama\", \"frilled-necked lizard\", \"alligator lizard\", \"Gila monster\", \"European green lizard\", \"chameleon\", \"Komodo dragon\", \"Nile crocodile\", \"American alligator\", \"triceratops\", \"worm snake\", \"ring-necked snake\", \"eastern hog-nosed snake\", \"smooth green snake\", \"kingsnake\", \"garter snake\", \"water snake\", \"vine snake\", \"night snake\", \"boa constrictor\", \"African rock python\", \"Indian cobra\", \"green mamba\", \"sea snake\", \"Saharan horned viper\", \"eastern diamondback rattlesnake\", \"sidewinder rattlesnake\", \"trilobite\", \"harvestman\", \"scorpion\", \"yellow garden spider\", \"barn spider\", \"European garden spider\", \"southern black widow\", \"tarantula\", \"wolf spider\", \"tick\", \"centipede\", \"black grouse\", \"ptarmigan\", \"ruffed grouse\", \"prairie grouse\", \"peafowl\", \"quail\", \"partridge\", \"african grey parrot\", \"macaw\", \"sulphur-crested cockatoo\", \"lorikeet\", \"coucal\", \"bee eater\", \"hornbill\", \"hummingbird\", \"jacamar\", \"toucan\", \"duck\", \"red-breasted merganser\", \"goose\", \"black swan\", \"tusker\", \"echidna\", \"platypus\", \"wallaby\", \"koala\", \"wombat\", \"jellyfish\", \"sea anemone\", \"brain coral\", \"flatworm\", \"nematode\", \"conch\", \"snail\", \"slug\", \"sea slug\", \"chiton\", \"chambered nautilus\", \"Dungeness crab\", \"rock crab\", \"fiddler crab\", \"red king crab\", \"American lobster\", \"spiny lobster\", \"crayfish\", \"hermit crab\", \"isopod\", \"white stork\", \"black stork\", \"spoonbill\", \"flamingo\", \"little blue heron\", \"great egret\", \"bittern bird\", \"crane bird\", \"limpkin\", \"common gallinule\", \"American coot\", \"bustard\", \"ruddy turnstone\", \"dunlin\", \"common redshank\", \"dowitcher\", \"oystercatcher\", \"pelican\", \"king penguin\", \"albatross\", \"grey whale\", \"killer whale\", \"dugong\", \"sea lion\", \"Chihuahua\", \"Japanese Chin\", \"Maltese\", \"Pekingese\", \"Shih Tzu\", \"King Charles Spaniel\", \"Papillon\", \"toy terrier\", \"Rhodesian Ridgeback\", \"Afghan Hound\", \"Basset Hound\", \"Beagle\", \"Bloodhound\", \"Bluetick Coonhound\", \"Black and Tan Coonhound\", \"Treeing Walker Coonhound\", \"English foxhound\", \"Redbone Coonhound\", \"borzoi\", \"Irish Wolfhound\", \"Italian Greyhound\", \"Whippet\", \"Ibizan Hound\", \"Norwegian Elkhound\", \"Otterhound\", \"Saluki\", \"Scottish Deerhound\", \"Weimaraner\", \"Staffordshire Bull Terrier\", \"American Staffordshire Terrier\", \"Bedlington Terrier\", \"Border Terrier\", \"Kerry Blue Terrier\", \"Irish Terrier\", \"Norfolk Terrier\", \"Norwich Terrier\", \"Yorkshire Terrier\", \"Wire Fox Terrier\", \"Lakeland Terrier\", \"Sealyham Terrier\", \"Airedale Terrier\", \"Cairn Terrier\", \"Australian Terrier\", \"Dandie Dinmont Terrier\", \"Boston Terrier\", \"Miniature Schnauzer\", \"Giant Schnauzer\", \"Standard Schnauzer\", \"Scottish Terrier\", \"Tibetan Terrier\", \"Australian Silky Terrier\", \"Soft-coated Wheaten Terrier\", \"West Highland White Terrier\", \"Lhasa Apso\", \"Flat-Coated Retriever\", \"Curly-coated Retriever\", \"Golden Retriever\", \"Labrador Retriever\", \"Chesapeake Bay Retriever\", \"German Shorthaired Pointer\", \"Vizsla\", \"English Setter\", \"Irish Setter\", \"Gordon Setter\", \"Brittany dog\", \"Clumber Spaniel\", \"English Springer Spaniel\", \"Welsh Springer Spaniel\", \"Cocker Spaniel\", \"Sussex Spaniel\", \"Irish Water Spaniel\", \"Kuvasz\", \"Schipperke\", \"Groenendael dog\", \"Malinois\", \"Briard\", \"Australian Kelpie\", \"Komondor\", \"Old English Sheepdog\", \"Shetland Sheepdog\", \"collie\", \"Border Collie\", \"Bouvier des Flandres dog\", \"Rottweiler\", \"German Shepherd Dog\", \"Dobermann\", \"Miniature Pinscher\", \"Greater Swiss Mountain Dog\", \"Bernese Mountain Dog\", \"Appenzeller Sennenhund\", \"Entlebucher Sennenhund\", \"Boxer\", \"Bullmastiff\", \"Tibetan Mastiff\", \"French Bulldog\", \"Great Dane\", \"St. Bernard\", \"husky\", \"Alaskan Malamute\", \"Siberian Husky\", \"Dalmatian\", \"Affenpinscher\", \"Basenji\", \"pug\", \"Leonberger\", \"Newfoundland dog\", \"Great Pyrenees dog\", \"Samoyed\", \"Pomeranian\", \"Chow Chow\", \"Keeshond\", \"brussels griffon\", \"Pembroke Welsh Corgi\", \"Cardigan Welsh Corgi\", \"Toy Poodle\", \"Miniature Poodle\", \"Standard Poodle\", \"Mexican hairless dog (xoloitzcuintli)\", \"grey wolf\", \"Alaskan tundra wolf\", \"red wolf or maned wolf\", \"coyote\", \"dingo\", \"dhole\", \"African wild dog\", \"hyena\", \"red fox\", \"kit fox\", \"Arctic fox\", \"grey fox\", \"tabby cat\", \"tiger cat\", \"Persian cat\", \"Siamese cat\", \"Egyptian Mau\", \"cougar\", \"lynx\", \"leopard\", \"snow leopard\", \"jaguar\", \"lion\", \"tiger\", \"cheetah\", \"brown bear\", \"American black bear\", \"polar bear\", \"sloth bear\", \"mongoose\", \"meerkat\", \"tiger beetle\", \"ladybug\", \"ground beetle\", \"longhorn beetle\", \"leaf beetle\", \"dung beetle\", \"rhinoceros beetle\", \"weevil\", \"fly\", \"bee\", \"ant\", \"grasshopper\", \"cricket insect\", \"stick insect\", \"cockroach\", \"praying mantis\", \"cicada\", \"leafhopper\", \"lacewing\", \"dragonfly\", \"damselfly\", \"red admiral butterfly\", \"ringlet butterfly\", \"monarch butterfly\", \"small white butterfly\", \"sulphur butterfly\", \"gossamer-winged butterfly\", \"starfish\", \"sea urchin\", \"sea cucumber\", \"cottontail rabbit\", \"hare\", \"Angora rabbit\", \"hamster\", \"porcupine\", \"fox squirrel\", \"marmot\", \"beaver\", \"guinea pig\", \"common sorrel horse\", \"zebra\", \"pig\", \"wild boar\", \"warthog\", \"hippopotamus\", \"ox\", \"water buffalo\", \"bison\", \"ram (adult male sheep)\", \"bighorn sheep\", \"Alpine ibex\", \"hartebeest\", \"impala (antelope)\", \"gazelle\", \"arabian camel\", \"llama\", \"weasel\", \"mink\", \"European polecat\", \"black-footed ferret\", \"otter\", \"skunk\", \"badger\", \"armadillo\", \"three-toed sloth\", \"orangutan\", \"gorilla\", \"chimpanzee\", \"gibbon\", \"siamang\", \"guenon\", \"patas monkey\", \"baboon\", \"macaque\", \"langur\", \"black-and-white colobus\", \"proboscis monkey\", \"marmoset\", \"white-headed capuchin\", \"howler monkey\", \"titi monkey\", \"Geoffroy's spider monkey\", \"common squirrel monkey\", \"ring-tailed lemur\", \"indri\", \"Asian elephant\", \"African bush elephant\", \"red panda\", \"giant panda\", \"snoek fish\", \"eel\", \"silver salmon\", \"rock beauty fish\", \"clownfish\", \"sturgeon\", \"gar fish\", \"lionfish\", \"pufferfish\", \"abacus\", \"abaya\", \"academic gown\", \"accordion\", \"acoustic guitar\", \"aircraft carrier\", \"airliner\", \"airship\", \"altar\", \"ambulance\", \"amphibious vehicle\", \"analog clock\", \"apiary\", \"apron\", \"trash can\", \"assault rifle\", \"backpack\", \"bakery\", \"balance beam\", \"balloon\", \"ballpoint pen\", \"Band-Aid\", \"banjo\", \"baluster / handrail\", \"barbell\", \"barber chair\", \"barbershop\", \"barn\", \"barometer\", \"barrel\", \"wheelbarrow\", \"baseball\", \"basketball\", \"bassinet\", \"bassoon\", \"swimming cap\", \"bath towel\", \"bathtub\", \"station wagon\", \"lighthouse\", \"beaker\", \"military hat (bearskin or shako)\", \"beer bottle\", \"beer glass\", \"bell tower\", \"baby bib\", \"tandem bicycle\", \"bikini\", \"ring binder\", \"binoculars\", \"birdhouse\", \"boathouse\", \"bobsleigh\", \"bolo tie\", \"poke bonnet\", \"bookcase\", \"bookstore\", \"bottle cap\", \"hunting bow\", \"bow tie\", \"brass memorial plaque\", \"bra\", \"breakwater\", \"breastplate\", \"broom\", \"bucket\", \"buckle\", \"bulletproof vest\", \"high-speed train\", \"butcher shop\", \"taxicab\", \"cauldron\", \"candle\", \"cannon\", \"canoe\", \"can opener\", \"cardigan\", \"car mirror\", \"carousel\", \"tool kit\", \"cardboard box / carton\", \"car wheel\", \"automated teller machine\", \"cassette\", \"cassette player\", \"castle\", \"catamaran\", \"CD player\", \"cello\", \"mobile phone\", \"chain\", \"chain-link fence\", \"chain mail\", \"chainsaw\", \"storage chest\", \"chiffonier\", \"bell or wind chime\", \"china cabinet\", \"Christmas stocking\", \"church\", \"movie theater\", \"cleaver\", \"cliff dwelling\", \"cloak\", \"clogs\", \"cocktail shaker\", \"coffee mug\", \"coffeemaker\", \"spiral or coil\", \"combination lock\", \"computer keyboard\", \"candy store\", \"container ship\", \"convertible\", \"corkscrew\", \"cornet\", \"cowboy boot\", \"cowboy hat\", \"cradle\", \"construction crane\", \"crash helmet\", \"crate\", \"infant bed\", \"Crock Pot\", \"croquet ball\", \"crutch\", \"cuirass\", \"dam\", \"desk\", \"desktop computer\", \"rotary dial telephone\", \"diaper\", \"digital clock\", \"digital watch\", \"dining table\", \"dishcloth\", \"dishwasher\", \"disc brake\", \"dock\", \"dog sled\", \"dome\", \"doormat\", \"drilling rig\", \"drum\", \"drumstick\", \"dumbbell\", \"Dutch oven\", \"electric fan\", \"electric guitar\", \"electric locomotive\", \"entertainment center\", \"envelope\", \"espresso machine\", \"face powder\", \"feather boa\", \"filing cabinet\", \"fireboat\", \"fire truck\", \"fire screen\", \"flagpole\", \"flute\", \"folding chair\", \"football helmet\", \"forklift\", \"fountain\", \"fountain pen\", \"four-poster bed\", \"freight car\", \"French horn\", \"frying pan\", \"fur coat\", \"garbage truck\", \"gas mask or respirator\", \"gas pump\", \"goblet\", \"go-kart\", \"golf ball\", \"golf cart\", \"gondola\", \"gong\", \"gown\", \"grand piano\", \"greenhouse\", \"radiator grille\", \"grocery store\", \"guillotine\", \"hair clip\", \"hair spray\", \"half-track\", \"hammer\", \"hamper\", \"hair dryer\", \"hand-held computer\", \"handkerchief\", \"hard disk drive\", \"harmonica\", \"harp\", \"combine harvester\", \"hatchet\", \"holster\", \"home theater\", \"honeycomb\", \"hook\", \"hoop skirt\", \"gymnastic horizontal bar\", \"horse-drawn vehicle\", \"hourglass\", \"iPod\", \"clothes iron\", \"carved pumpkin\", \"jeans\", \"jeep\", \"T-shirt\", \"jigsaw puzzle\", \"rickshaw\", \"joystick\", \"kimono\", \"knee pad\", \"knot\", \"lab coat\", \"ladle\", \"lampshade\", \"laptop computer\", \"lawn mower\", \"lens cap\", \"letter opener\", \"library\", \"lifeboat\", \"lighter\", \"limousine\", \"ocean liner\", \"lipstick\", \"slip-on shoe\", \"lotion\", \"music speaker\", \"loupe magnifying glass\", \"sawmill\", \"magnetic compass\", \"messenger bag\", \"mailbox\", \"tights\", \"one-piece bathing suit\", \"manhole cover\", \"maraca\", \"marimba\", \"mask\", \"matchstick\", \"maypole\", \"maze\", \"measuring cup\", \"medicine cabinet\", \"megalith\", \"microphone\", \"microwave oven\", \"military uniform\", \"milk can\", \"minibus\", \"miniskirt\", \"minivan\", \"missile\", \"mitten\", \"mixing bowl\", \"mobile home\", \"ford model t\", \"modem\", \"monastery\", \"monitor\", \"moped\", \"mortar and pestle\", \"graduation cap\", \"mosque\", \"mosquito net\", \"vespa\", \"mountain bike\", \"tent\", \"computer mouse\", \"mousetrap\", \"moving van\", \"muzzle\", \"metal nail\", \"neck brace\", \"necklace\", \"baby pacifier\", \"notebook computer\", \"obelisk\", \"oboe\", \"ocarina\", \"odometer\", \"oil filter\", \"pipe organ\", \"oscilloscope\", \"overskirt\", \"bullock cart\", \"oxygen mask\", \"product packet / packaging\", \"paddle\", \"paddle wheel\", \"padlock\", \"paintbrush\", \"pajamas\", \"palace\", \"pan flute\", \"paper towel\", \"parachute\", \"parallel bars\", \"park bench\", \"parking meter\", \"railroad car\", \"patio\", \"payphone\", \"pedestal\", \"pencil case\", \"pencil sharpener\", \"perfume\", \"Petri dish\", \"photocopier\", \"plectrum\", \"Pickelhaube\", \"picket fence\", \"pickup truck\", \"pier\", \"piggy bank\", \"pill bottle\", \"pillow\", \"ping-pong ball\", \"pinwheel\", \"pirate ship\", \"drink pitcher\", \"block plane\", \"planetarium\", \"plastic bag\", \"plate rack\", \"farm plow\", \"plunger\", \"Polaroid camera\", \"pole\", \"police van\", \"poncho\", \"pool table\", \"soda bottle\", \"plant pot\", \"potter's wheel\", \"power drill\", \"prayer rug\", \"printer\", \"prison\", \"missile\", \"projector\", \"hockey puck\", \"punching bag\", \"purse\", \"quill\", \"quilt\", \"race car\", \"racket\", \"radiator\", \"radio\", \"radio telescope\", \"rain barrel\", \"recreational vehicle\", \"fishing casting reel\", \"reflex camera\", \"refrigerator\", \"remote control\", \"restaurant\", \"revolver\", \"rifle\", \"rocking chair\", \"rotisserie\", \"eraser\", \"rugby ball\", \"ruler measuring stick\", \"sneaker\", \"safe\", \"safety pin\", \"salt shaker\", \"sandal\", \"sarong\", \"saxophone\", \"scabbard\", \"weighing scale\", \"school bus\", \"schooner\", \"scoreboard\", \"CRT monitor\", \"screw\", \"screwdriver\", \"seat belt\", \"sewing machine\", \"shield\", \"shoe store\", \"shoji screen / room divider\", \"shopping basket\", \"shopping cart\", \"shovel\", \"shower cap\", \"shower curtain\", \"ski\", \"balaclava ski mask\", \"sleeping bag\", \"slide rule\", \"sliding door\", \"slot machine\", \"snorkel\", \"snowmobile\", \"snowplow\", \"soap dispenser\", \"soccer ball\", \"sock\", \"solar thermal collector\", \"sombrero\", \"soup bowl\", \"keyboard space bar\", \"space heater\", \"space shuttle\", \"spatula\", \"motorboat\", \"spider web\", \"spindle\", \"sports car\", \"spotlight\", \"stage\", \"steam locomotive\", \"through arch bridge\", \"steel drum\", \"stethoscope\", \"scarf\", \"stone wall\", \"stopwatch\", \"stove\", \"strainer\", \"tram\", \"stretcher\", \"couch\", \"stupa\", \"submarine\", \"suit\", \"sundial\", \"sunglasses\", \"sunglasses\", \"sunscreen\", \"suspension bridge\", \"mop\", \"sweatshirt\", \"swim trunks / shorts\", \"swing\", \"electrical switch\", \"syringe\", \"table lamp\", \"tank\", \"tape player\", \"teapot\", \"teddy bear\", \"television\", \"tennis ball\", \"thatched roof\", \"front curtain\", \"thimble\", \"threshing machine\", \"throne\", \"tile roof\", \"toaster\", \"tobacco shop\", \"toilet seat\", \"torch\", \"totem pole\", \"tow truck\", \"toy store\", \"tractor\", \"semi-trailer truck\", \"tray\", \"trench coat\", \"tricycle\", \"trimaran\", \"tripod\", \"triumphal arch\", \"trolleybus\", \"trombone\", \"hot tub\", \"turnstile\", \"typewriter keyboard\", \"umbrella\", \"unicycle\", \"upright piano\", \"vacuum cleaner\", \"vase\", \"vaulted or arched ceiling\", \"velvet fabric\", \"vending machine\", \"vestment\", \"viaduct\", \"violin\", \"volleyball\", \"waffle iron\", \"wall clock\", \"wallet\", \"wardrobe\", \"military aircraft\", \"sink\", \"washing machine\", \"water bottle\", \"water jug\", \"water tower\", \"whiskey jug\", \"whistle\", \"hair wig\", \"window screen\", \"window shade\", \"Windsor tie\", \"wine bottle\", \"airplane wing\", \"wok\", \"wooden spoon\", \"wool\", \"split-rail fence\", \"shipwreck\", \"sailboat\", \"yurt\", \"website\", \"comic book\", \"crossword\", \"traffic or street sign\", \"traffic light\", \"dust jacket\", \"menu\", \"plate\", \"guacamole\", \"consomme\", \"hot pot\", \"trifle\", \"ice cream\", \"popsicle\", \"baguette\", \"bagel\", \"pretzel\", \"cheeseburger\", \"hot dog\", \"mashed potatoes\", \"cabbage\", \"broccoli\", \"cauliflower\", \"zucchini\", \"spaghetti squash\", \"acorn squash\", \"butternut squash\", \"cucumber\", \"artichoke\", \"bell pepper\", \"cardoon\", \"mushroom\", \"Granny Smith apple\", \"strawberry\", \"orange\", \"lemon\", \"fig\", \"pineapple\", \"banana\", \"jackfruit\", \"cherimoya (custard apple)\", \"pomegranate\", \"hay\", \"carbonara\", \"chocolate syrup\", \"dough\", \"meatloaf\", \"pizza\", \"pot pie\", \"burrito\", \"red wine\", \"espresso\", \"tea cup\", \"eggnog\", \"mountain\", \"bubble\", \"cliff\", \"coral reef\", \"geyser\", \"lakeshore\", \"promontory\", \"sandbar\", \"beach\", \"valley\", \"volcano\", \"baseball player\", \"bridegroom\", \"scuba diver\", \"rapeseed\", \"daisy\", \"yellow lady's slipper\", \"corn\", \"acorn\", \"rose hip\", \"horse chestnut seed\", \"coral fungus\", \"agaric\", \"gyromitra\", \"stinkhorn mushroom\", \"earth star fungus\", \"hen of the woods mushroom\", \"bolete\", \"corn cob\", \"toilet paper\"]\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7e077342", "metadata": {}, "outputs": [], "source": [ "# The following comes from here: https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/zero_shot_metadata.py\n", "# Original reference: https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb\n", "openai_imagenet_templates = (\n", " lambda c: f\"a bad photo of a {c}.\",\n", " lambda c: f\"a photo of many {c}.\",\n", " lambda c: f\"a sculpture of a {c}.\",\n", " lambda c: f\"a photo of the hard to see {c}.\",\n", " lambda c: f\"a low resolution photo of the {c}.\",\n", " lambda c: f\"a rendering of a {c}.\",\n", " lambda c: f\"graffiti of a {c}.\",\n", " lambda c: f\"a bad photo of the {c}.\",\n", " lambda c: f\"a cropped photo of the {c}.\",\n", " lambda c: f\"a tattoo of a {c}.\",\n", " lambda c: f\"the embroidered {c}.\",\n", " lambda c: f\"a photo of a hard to see {c}.\",\n", " lambda c: f\"a bright photo of a {c}.\",\n", " lambda c: f\"a photo of a clean {c}.\",\n", " lambda c: f\"a photo of a dirty {c}.\",\n", " lambda c: f\"a dark photo of the {c}.\",\n", " lambda c: f\"a drawing of a {c}.\",\n", " lambda c: f\"a photo of my {c}.\",\n", " lambda c: f\"the plastic {c}.\",\n", " lambda c: f\"a photo of the cool {c}.\",\n", " lambda c: f\"a close-up photo of a {c}.\",\n", " lambda c: f\"a black and white photo of the {c}.\",\n", " lambda c: f\"a painting of the {c}.\",\n", " lambda c: f\"a painting of a {c}.\",\n", " lambda c: f\"a pixelated photo of the {c}.\",\n", " lambda c: f\"a sculpture of the {c}.\",\n", " lambda c: f\"a bright photo of the {c}.\",\n", " lambda c: f\"a cropped photo of a {c}.\",\n", " lambda c: f\"a plastic {c}.\",\n", " lambda c: f\"a photo of the dirty {c}.\",\n", " lambda c: f\"a jpeg corrupted photo of a {c}.\",\n", " lambda c: f\"a blurry photo of the {c}.\",\n", " lambda c: f\"a photo of the {c}.\",\n", " lambda c: f\"a good photo of the {c}.\",\n", " lambda c: f\"a rendering of the {c}.\",\n", " lambda c: f\"a {c} in a video game.\",\n", " lambda c: f\"a photo of one {c}.\",\n", " lambda c: f\"a doodle of a {c}.\",\n", " lambda c: f\"a close-up photo of the {c}.\",\n", " lambda c: f\"a photo of a {c}.\",\n", " lambda c: f\"the origami {c}.\",\n", " lambda c: f\"the {c} in a video game.\",\n", " lambda c: f\"a sketch of a {c}.\",\n", " lambda c: f\"a doodle of the {c}.\",\n", " lambda c: f\"a origami {c}.\",\n", " lambda c: f\"a low resolution photo of a {c}.\",\n", " lambda c: f\"the toy {c}.\",\n", " lambda c: f\"a rendition of the {c}.\",\n", " lambda c: f\"a photo of the clean {c}.\",\n", " lambda c: f\"a photo of a large {c}.\",\n", " lambda c: f\"a rendition of a {c}.\",\n", " lambda c: f\"a photo of a nice {c}.\",\n", " lambda c: f\"a photo of a weird {c}.\",\n", " lambda c: f\"a blurry photo of a {c}.\",\n", " lambda c: f\"a cartoon {c}.\",\n", " lambda c: f\"art of a {c}.\",\n", " lambda c: f\"a sketch of the {c}.\",\n", " lambda c: f\"a embroidered {c}.\",\n", " lambda c: f\"a pixelated photo of a {c}.\",\n", " lambda c: f\"itap of the {c}.\",\n", " lambda c: f\"a jpeg corrupted photo of the {c}.\",\n", " lambda c: f\"a good photo of a {c}.\",\n", " lambda c: f\"a plushie {c}.\",\n", " lambda c: f\"a photo of the nice {c}.\",\n", " lambda c: f\"a photo of the small {c}.\",\n", " lambda c: f\"a photo of the weird {c}.\",\n", " lambda c: f\"the cartoon {c}.\",\n", " lambda c: f\"art of the {c}.\",\n", " lambda c: f\"a drawing of the {c}.\",\n", " lambda c: f\"a photo of the large {c}.\",\n", " lambda c: f\"a black and white photo of a {c}.\",\n", " lambda c: f\"the plushie {c}.\",\n", " lambda c: f\"a dark photo of a {c}.\",\n", " lambda c: f\"itap of a {c}.\",\n", " lambda c: f\"graffiti of the {c}.\",\n", " lambda c: f\"a toy {c}.\",\n", " lambda c: f\"itap of my {c}.\",\n", " lambda c: f\"a photo of a cool {c}.\",\n", " lambda c: f\"a photo of a small {c}.\",\n", " lambda c: f\"a tattoo of the {c}.\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "c080a969", "metadata": {}, "outputs": [], "source": [ "from torchvision.datasets import ImageFolder\n", "# Please update the following directory to the root of ImageNet1k val dataset.\n", "imagenet_val_root_dir = \"\"\n", "val_dataset = ImageFolder(imagenet_val_root_dir, image_preprocess)" ] }, { "cell_type": "code", "execution_count": null, "id": "0cc1d9d9", "metadata": {}, "outputs": [], "source": [ "def zeroshot_classifier(classnames, templates, tokenizer):\n", " with torch.no_grad():\n", " zeroshot_weights = []\n", " for classname in classnames:\n", " texts = [template(classname) for template in templates] #format with class\n", " texts = tokenizer.tokenize(texts).cuda() #tokenize\n", " class_embeddings = model.encode_text(texts) #embed with text encoder\n", " class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)\n", " class_embedding = class_embeddings.mean(dim=0)\n", " class_embedding /= class_embedding.norm()\n", " zeroshot_weights.append(class_embedding)\n", " zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()\n", " return zeroshot_weights\n", "zeroshot_weights = zeroshot_classifier(imagenet_clip_class_names, openai_imagenet_templates, tokenizer)" ] }, { "cell_type": "code", "execution_count": null, "id": "e7e3523e", "metadata": {}, "outputs": [], "source": [ "class_name_to_ids = {class_name:idx for idx, class_name in enumerate(imagenet_clip_class_names)}" ] }, { "cell_type": "code", "execution_count": null, "id": "eef6c047", "metadata": {}, "outputs": [], "source": [ "def accuracy(output, target, topk=(1,)):\n", " pred = output.topk(max(topk), 1, True, True)[1].t()\n", " correct = pred.eq(target.view(1, -1).expand_as(pred))\n", " return [correct[:k].reshape(-1).sum(0, keepdim=True) for k in topk]" ] }, { "cell_type": "code", "execution_count": null, "id": "6d2a9ff8", "metadata": {}, "outputs": [], "source": [ "from tqdm.notebook import tqdm\n", "\n", "images = []\n", "class_ids = []\n", "batch_size = 64\n", "num_workers = 8\n", "top1, top5, n = 0., 0., 0.\n", "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True)\n", "for images, targets in tqdm(val_loader):\n", " with torch.autocast('cuda', dtype=torch.float):\n", " with torch.no_grad():\n", " image_features = model.encode_image(images.cuda())\n", " image_features /= image_features.norm(dim=-1, keepdim=True)\n", " logits = 100. * image_features @ zeroshot_weights\n", " acc1, acc5 = accuracy(logits, targets.cuda(), topk=(1, 5))\n", " top1 += acc1\n", " top5 += acc5\n", " n += len(images)\n", " images = []\n", " class_ids = []\n", "top1 = (top1.item() / n) * 100\n", "top5 = (top5.item() / n) * 100 \n", "print(f\"Top-1 accuracy: {top1}\")\n", "print(f\"Top-5 accuracy: {top5}\")" ] } ], "metadata": { "kernelspec": { "display_name": "fairvit-py311-ptnightly-xformers-20250129", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/semantic_segmentation.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "b470389d-a897-416e-9601-aeacb39cd694", "metadata": {}, "outputs": [], "source": [ "# Copyright (c) Meta Platforms, Inc. and affiliates." ] }, { "cell_type": "markdown", "id": "eb5c8577-7dff-41b1-9b04-2dca12940e02", "metadata": {}, "source": [ "# Semantic Segmentation \"Open" ] }, { "cell_type": "code", "execution_count": 2, "id": "febdf412-5ad0-4bbc-9530-754f92dcc491", "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "INSTALL = False # Switch this to install dependencies\n", "if INSTALL: # Try installing package with extras\n", " REPO_URL = \"https://github.com/facebookresearch/dinov2\"\n", " !{sys.executable} -m pip install -e {REPO_URL}'[extras]' --extra-index-url https://download.pytorch.org/whl/cu117 --extra-index-url https://pypi.nvidia.com\n", "else:\n", " REPO_PATH = \"\" # Specify a local path to the repository (or use installed package instead)\n", " sys.path.append(REPO_PATH)" ] }, { "cell_type": "markdown", "id": "efdf378b-0591-4879-9db6-6a4ab582d49f", "metadata": {}, "source": [ "## Utilities" ] }, { "cell_type": "code", "execution_count": 3, "id": "90223c04-e7da-4738-bb16-d4f7025aa3eb", "metadata": {}, "outputs": [], "source": [ "import math\n", "import itertools\n", "from functools import partial\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "from mmseg.apis import init_segmentor, inference_segmentor\n", "\n", "import dinov2.eval.segmentation.models\n", "\n", "\n", "class CenterPadding(torch.nn.Module):\n", " def __init__(self, multiple):\n", " super().__init__()\n", " self.multiple = multiple\n", "\n", " def _get_pad(self, size):\n", " new_size = math.ceil(size / self.multiple) * self.multiple\n", " pad_size = new_size - size\n", " pad_size_left = pad_size // 2\n", " pad_size_right = pad_size - pad_size_left\n", " return pad_size_left, pad_size_right\n", "\n", " @torch.inference_mode()\n", " def forward(self, x):\n", " pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))\n", " output = F.pad(x, pads)\n", " return output\n", "\n", "\n", "def create_segmenter(cfg, backbone_model):\n", " model = init_segmentor(cfg)\n", " model.backbone.forward = partial(\n", " backbone_model.get_intermediate_layers,\n", " n=cfg.model.backbone.out_indices,\n", " reshape=True,\n", " )\n", " if hasattr(backbone_model, \"patch_size\"):\n", " model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone_model.patch_size)(x[0]))\n", " model.init_weights()\n", " return model" ] }, { "cell_type": "markdown", "id": "a5724efc-b2b8-46ed-94e1-7fee59a39ed9", "metadata": {}, "source": [ "## Load pretrained backbone" ] }, { "cell_type": "code", "execution_count": 4, "id": "2d51b932-1157-45ce-997f-572ad417a12f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using cache found in /private/home/plabatut/.cache/torch/hub/facebookresearch_dinov2_main\n", "/private/home/plabatut/github/patricklabatut/dinov2/dinov2/layers/swiglu_ffn.py:43: UserWarning: xFormers is available (SwiGLU)\n", " warnings.warn(\"xFormers is available (SwiGLU)\")\n", "/private/home/plabatut/github/patricklabatut/dinov2/dinov2/layers/attention.py:27: UserWarning: xFormers is available (Attention)\n", " warnings.warn(\"xFormers is available (Attention)\")\n", "/private/home/plabatut/github/patricklabatut/dinov2/dinov2/layers/block.py:33: UserWarning: xFormers is available (Block)\n", " warnings.warn(\"xFormers is available (Block)\")\n" ] }, { "data": { "text/plain": [ "DinoVisionTransformer(\n", " (patch_embed): PatchEmbed(\n", " (proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14))\n", " (norm): Identity()\n", " )\n", " (blocks): ModuleList(\n", " (0-11): 12 x NestedTensorBlock(\n", " (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", " (attn): MemEffAttention(\n", " (qkv): Linear(in_features=384, out_features=1152, bias=True)\n", " (attn_drop): Dropout(p=0.0, inplace=False)\n", " (proj): Linear(in_features=384, out_features=384, bias=True)\n", " (proj_drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (ls1): LayerScale()\n", " (drop_path1): Identity()\n", " (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", " (mlp): Mlp(\n", " (fc1): Linear(in_features=384, out_features=1536, bias=True)\n", " (act): GELU(approximate='none')\n", " (fc2): Linear(in_features=1536, out_features=384, bias=True)\n", " (drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (ls2): LayerScale()\n", " (drop_path2): Identity()\n", " )\n", " )\n", " (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", " (head): Identity()\n", ")" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BACKBONE_SIZE = \"small\" # in (\"small\", \"base\", \"large\" or \"giant\")\n", "\n", "\n", "backbone_archs = {\n", " \"small\": \"vits14\",\n", " \"base\": \"vitb14\",\n", " \"large\": \"vitl14\",\n", " \"giant\": \"vitg14\",\n", "}\n", "backbone_arch = backbone_archs[BACKBONE_SIZE]\n", "backbone_name = f\"dinov2_{backbone_arch}\"\n", "\n", "backbone_model = torch.hub.load(repo_or_dir=\"facebookresearch/dinov2\", model=backbone_name)\n", "backbone_model.eval()\n", "backbone_model.cuda()" ] }, { "cell_type": "markdown", "id": "c1c90501-d6ef-436e-b1a1-72e63b0534e3", "metadata": {}, "source": [ "## Load pretrained segmentation head" ] }, { "cell_type": "code", "execution_count": 5, "id": "d0bf0b7f-ad98-4cfb-8120-f076df8f8933", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/private/home/plabatut/.conda/envs/dinov2-extras-conda/lib/python3.9/site-packages/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.\n", " warnings.warn(\n", "2023-08-31 06:29:03,743 - mmcv - INFO - initialize BNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n", "2023-08-31 06:29:03,744 - mmcv - INFO - \n", "decode_head.conv_seg.weight - torch.Size([21, 1536, 1, 1]): \n", "NormalInit: mean=0, std=0.01, bias=0 \n", " \n", "2023-08-31 06:29:03,745 - mmcv - INFO - \n", "decode_head.conv_seg.bias - torch.Size([21]): \n", "NormalInit: mean=0, std=0.01, bias=0 \n", " \n", "2023-08-31 06:29:03,745 - mmcv - INFO - \n", "decode_head.bn.weight - torch.Size([1536]): \n", "The value is the same before and after calling `init_weights` of EncoderDecoder \n", " \n", "2023-08-31 06:29:03,746 - mmcv - INFO - \n", "decode_head.bn.bias - torch.Size([1536]): \n", "The value is the same before and after calling `init_weights` of EncoderDecoder \n", " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "scales: [1.0, 1.32, 1.73]\n", "load checkpoint from http path: https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_voc2012_ms_head.pth\n" ] }, { "data": { "text/plain": [ "EncoderDecoder(\n", " (backbone): DinoVisionTransformer()\n", " (decode_head): BNHead(\n", " input_transform=resize_concat, ignore_index=255, align_corners=False\n", " (loss_decode): CrossEntropyLoss(avg_non_ignore=False)\n", " (conv_seg): Conv2d(1536, 21, kernel_size=(1, 1), stride=(1, 1))\n", " (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}\n", ")" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import urllib\n", "\n", "import mmcv\n", "from mmcv.runner import load_checkpoint\n", "\n", "\n", "def load_config_from_url(url: str) -> str:\n", " with urllib.request.urlopen(url) as f:\n", " return f.read().decode()\n", "\n", "\n", "HEAD_SCALE_COUNT = 3 # more scales: slower but better results, in (1,2,3,4,5)\n", "HEAD_DATASET = \"voc2012\" # in (\"ade20k\", \"voc2012\")\n", "HEAD_TYPE = \"ms\" # in (\"ms, \"linear\")\n", "\n", "\n", "DINOV2_BASE_URL = \"https://dl.fbaipublicfiles.com/dinov2\"\n", "head_config_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_config.py\"\n", "head_checkpoint_url = f\"{DINOV2_BASE_URL}/{backbone_name}/{backbone_name}_{HEAD_DATASET}_{HEAD_TYPE}_head.pth\"\n", "\n", "cfg_str = load_config_from_url(head_config_url)\n", "cfg = mmcv.Config.fromstring(cfg_str, file_format=\".py\")\n", "if HEAD_TYPE == \"ms\":\n", " cfg.data.test.pipeline[1][\"img_ratios\"] = cfg.data.test.pipeline[1][\"img_ratios\"][:HEAD_SCALE_COUNT]\n", " print(\"scales:\", cfg.data.test.pipeline[1][\"img_ratios\"])\n", "\n", "model = create_segmenter(cfg, backbone_model=backbone_model)\n", "load_checkpoint(model, head_checkpoint_url, map_location=\"cpu\")\n", "model.cuda()\n", "model.eval()" ] }, { "cell_type": "markdown", "id": "2dc1b106-d28c-41cc-9ddd-f558d66a4715", "metadata": {}, "source": [ "## Load sample image" ] }, { "cell_type": "code", "execution_count": 6, "id": "44511634-8243-4662-a512-4531014adb32", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import urllib\n", "\n", "from PIL import Image\n", "\n", "\n", "def load_image_from_url(url: str) -> Image:\n", " with urllib.request.urlopen(url) as f:\n", " return Image.open(f).convert(\"RGB\")\n", "\n", "\n", "EXAMPLE_IMAGE_URL = \"https://dl.fbaipublicfiles.com/dinov2/images/example.jpg\"\n", "\n", "\n", "image = load_image_from_url(EXAMPLE_IMAGE_URL)\n", "display(image)" ] }, { "cell_type": "markdown", "id": "7e3240cb-54d0-438d-99e8-8c1af534f830", "metadata": {}, "source": [ "## Semantic segmentation on sample image" ] }, { "cell_type": "code", "execution_count": 7, "id": "49226d5b-83fc-4cfb-ba06-407bb2c0d96f", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAIAAAC6s0uzAAAXVklEQVR4nO3dXXbiuraAUdcZaViaRtPStPPAHi6KgPGPpLUkzfl0x7m1EweIPi/ZgT+3BWAKP7cP/+D70z848TU/2vim5774963AUdVw4uFN4vfjWeRn+V+BrwEAHCTAABBAgAH+E7JzW/yb5tx/5jcBBqCFkc4MivwsAgxw0khFaeMn6w1iG+od8J9qXxkgkf3L6J4bXI8uynvuTP79fbtr1SFd3BS9/RRc/BEEGJhCkQAf+iLrP376gvtLPHaAV2lLvOfxv3LwtqCB8R0q2ct/fHrv9PcCnbY3URKeZ7TZKjcBA4M7vZLeS3niP1//w+3WvvvKs03Aj7Z3C2qfu5Q6x9pJgIGRhTRsZ4CXTw2eMMA7FS/xxYf63PF8XfqeAInlD9i7m7PyH3msd9fXz32RKAIMjCl8ed3JsHvF6RKXfcDvX+3oMdiCBgYU27PTY5kMX7Tzka/3OB966k3AAAzi5ccWnRtPTx/A/m/kz5AAGNaa5IRvwiXAwGiyrbMEWufR7yOzaRsCDJBFtkL07v54/k5vksfZNWCARPa8a/T6Lx+Z+58kqewGEzBASdfX/XVue/f/fbmbmr83LUU9Goe+rwADQwkfBIscwEZ9T/xX5CTAwHTeDZFlBb4L5uR6eRC8EQcwlI3sFfmcwZ2u/PnpiU8OPvoVxrbnMc/wXhwCDIzm5doauCgfavC5O7CufKkhnfscqgbf+pEtaGA0p3cgK21d7nwLiITvFMEJ+59EEzAwoCufIxuyOXn6U4c/mjnqgR90sefZEWBgWPWuwp728mBOfzsNXiX8UKmPz44taGBYp2917uU2Wh6lqu8eJmCAt2rflnXx65uAk9t+grwVJcBblTY2WxZRfdMSYIAPEl5f3KO7A56NAAPssv9jEgLlP0JWbsIC2CvzzVn+jLg7AgxwQKoGr8WV3h65CxrgMMFjD3dBAxT29HdEZd9eg3reFTHkyTIBA9Siwak0/oSGj1crBBigLhkO1/4dS/Z8RzdhAdSV6r6tCbV//Hd+RwEGqE6Du1DkafJ5wAC5aHCIzA+7AAMwphP1vRjsQ/+5AAM0knkaG0/aS78rAQZoR4PbuPI4N/sMaQEGYCi9nOUIMADjaHwn85VvKsAATfUyn1GbAAPAeafPqAQYoDVDMIsAA4TQ4BoKPqoNniABBoihwWV193j6NCSASD4r6bp66f347Fz51iZggEjft9eLeHfzXIh3j14Rtc+Nvup+eQB2+L79t9zr7n7hj9XFAxBggBTCc9KXAR4uW9AAcNj1MwABBqAzA4y/iy1oAO4eq5b53uwM9S1yDCZggKRalib/p+de+a9OaHAKIsAAeYVMe2nH3wyz71LuMAQYgP+i+3NrV9+0mV9aHZt3wgLILnOrmkmy+VzwMNyEBQDL0vxExxY0AARsMwgwQHZJbj4aWMgmvwADQAABBoAAAgxAB8a7FVyAAehDpQZHpV2AAejGSHOwAAPQk5Zv1/Wk7O3oAgyQ3UhjXymlHpPAx9Y7YZHC93J7+b//vPnfAX5uBUbS77h5+kyAH9fKPevju7X1EAvxkD6+NtZ/4AXAtIy/G4o0OMrnAG8vke9iXCS6ew7Dutypo6+Q+7/3dMPw1qAOf+ax9WlIxSOagRU8UKlXlCeReQwfodXvQXb/z359CN75vcpO228n4CHru/z6uSzlVVV6FX0vN08c0LvXE/Co9d1mTb+u8SvHU8bY5hl/l8s7z22G4LoT8JzpvTt6cxl3ga8ZozAMI//ZRvG7vf5Yv7Z5fJ4kPEXzHDGq/E1K5WIgPz7axQPs74A/yPlnMNsVPHGoCbO6nzkYrnjsStfJ7+5PkkzAh8U+YkdL+fJou87tO17JjKdqDt+1qusG353L8PYPXiPtAnxe44duyGqW5cXMYEq18Gg83n3fK38p1F6pn/rEl9pJgKso/qiq735e0gzjet4KzoI9Tsz7f/z29V1cA67kRC/fZUN6j3JJGIo3o69rq3f5j1mAsxDagjSYadWozvbXDPwwgw2lHoeqFRdgxqTBTKhILZ5qmn+OvLtynFEnEP+L+bZQn00FuKiX+l4ROL4LMCPTYGggvNOnD6D9nx49EmAGp8GwX8KruR9Vqm8DrgEzPteDYY/wIFWV8KczATMFczDdaRyMhH3aY+f4m/OnE2BmocHwTs4+fdR1fRcBZioaDL+l7VOsBneWCTBz0WAoLiTh4bdeXyfAAAwr83z/v5/l9uMeUWZiCCa/Qx+HUEnmEbP2sbX52f9OwDLMPDSY7tyT0CyKRT5HqJLMZwaH2IJmUhpMp1LlJ/MG73L28Jo9ws8BNgQDxGpZtQx73dN6DrCxgHl4tdOjBnnOPNeOdHJgC5qpaTAzyxzal47W90StWwZegAE6UDyW21+wuzYX0Xi8/ifApgEm5GVPR1TzqMxb1iZggD6c6+vP7cV/2GOqx/jb30cCDIZghrWG9p7hQ939+I97qXjaIViAAcb0MpCHMtxLYq8LifTfABsCmJnXP7w0T4PbMwHDfzSY3j2OcQXDmeEqctpt5Cu+og8AgAJqJ2rgUTiq7v98GEPMIUAahmB69H17TsjAsRzJP1vQGgwaTF+G3Jt9MurP6BowQK9elsn4+2T7AQmsuwADJCKf8/BxhPDMLjQh1puNd85ko27MzsMEDJDLlSG45QDtDOCiP7dX/6sJgMnZCiKJjaD+7l9UfYt/3+Jpf3eEsecQJmB4wTko7R19r+ZAVbvVrL7hXgfY6T9AiNNv1NwyM2mTdkj4Frp3wgJIapjPI9ovPIot2YIG4LDx2t+eAAOkcB/+1hFwwsJNNf4uAgyQx2wFmpwAAzCdDOc6AgyQUYZCbEh+eF0QYACOqVHfCYv+9s+Qfpab9yIAYDVMI5P8IP4OGCCp7wTvjZWkVUOyBQ1QRXg7L/q+Vfxcpv1f/Lqnb5TnlGIrwN6QEuCKThtco46PX/P3F68dxfW756nvYgIGKOvpMxW6a3ClRGX4qIlU9V3efRzhI7diMSc7QFx07831Rb+Xj/i9eJzZ6tiACRiglr6icvFo+/phM/g8AS+GYGZlCCaJBkNwqXxeOdTZEi7A8JYAk0rxDNe73HvaVA3eFeBFg5mSAJNQF5daNXgP14ABetLyL2hDhN8s3czeABsFmJCNH9JKnuGLxzZJg03AAL1KnuErZmjwgfeC9vEMAP1qnOr12x1N6ainFL+ZgGGLk07ym6dYgxFgABKZYfP57liA3YoFkNDHIThqSp6npieYgOEDu9B0YSOx3e1RT5LtwwE2BAPk9Pum6IFvkx7AgbugAciveHEf59FmOf+Z4NRBgAH4z8e931Kfschy7hqwXWiAwfzcDlx5bXONdvgrwW7Cgs/chwVPNPi6kwE2BAMM41znmjV41AybgAE4qVkah8zw+QAbggFo2cWN73UvdF+dNgEDcEnjBj99u76i++hSgA3BACxvKlivi+u36zS9dyZgAMpYc9hmKu26vsv1ABuCAbpWNmP9bgi3ZwKGXfwpMHSho3fpKhBgQzAAHOW9oAHmtX+7+N1kacP5NAEGmNSedn7c0f120fesMteA7UIDdOfjpwXvvJ7a0WXXVEzAAFOTzyjuggbgKhU/oViA7UIDzCxDgzMcw34mYADK6Kt/4QQYgGICG9xd/ksG2C40AOxkAgagpPaT6Me/p8pJgGEXGzywX8sc9pjeu8IBtkgBsLTqYr/1XWpMwBoMwFK/jl3Xd7EFDUA9vTeyqj+3Ol/Xh6cyGFs7cFHZz2wYIO21JmCrFYNxTgkXdXqvcj22oAFoR4ZXFQNsCAbgJQ1eTMAAEEKAYS+XgaEgQ7AAA0CAugF2GRiA4saYnk3AAAQo+2fBPRJgAAggwHCA+7CgiCvj7xj7z0uDALsMDMAjm893JmAA2rlY32HG36VNgA3BAPDEBAzHuAwMFNEowIZgAK4b6fpxuwlYgwFg1XQLWoMZg11o4DrXgAFIZ6S7nd9pHWBDMMC0dl7Bvdd3+AabgAEgQECADcEAc/q+FZhrh7kR2gQMhzmJhCuKZHgAAgxAAA2OCbABAoCX1jAPs9X8jgkYgBiT70V/RR8AdMb+DZT1ssHDj79L4ARsFQPgpe36DjM024KGA5w4AqVEBthaBsC0TMAAEECAAejGMBeAFwGG/Vw0AQoSYAD6MNL4uwgw7GT8BcoKDrBFDYBHL/8IeMj3zPJOWPCZM0WIMl53V/Fb0JY2AO6ext+B67uYgAHIY+ziPomfgBdDMLl5fQI1pAgwAMzmzy36CFbf5gzyMf5Cbet136n2nxcTMABRfm7/3HU1w2cAP0o0AS+GYJIx/kJt7/7qdwa5ArxoMJkIMDTzu8TDZzjdFrQljyS8FKGl4XP7W7oAA8AMBBheMP5Ce7MNwRkDbO0jllcg0EDGAAPA8JIG2AhCFK89oI2kAQaAsQkw/GX8hSRmuCEr3RtxPPKmHDQmwEAzqSdgqyEAo0od4KVVg5WexcsAaOsr+gCCrWvu+n/Y9waggdTXgFeVovhx4hHjqZiAgZZmn4C3Pa3IegxQ1vohSDPc9vwk+zXguySjyc9yS3IkAPSujwBXcm6ilWEAruvjGvBd1JXgbfalh+G8Cmhp6gn47mJB7wOxtRuAQwR4WQpNsTIMwH49bUEv0fu9O/tqU7pHTp6AxjqbgGNXye/ltieulnIAPuoswL3QYAC29Rfg8Lbt3GF2SRiADf0FOIP9V3lluAueI6C9LgPc3XIpwwA86TLAS4IGn7jVWYYBWPUa4Ay8k+UYPB1AiI4DnGHdPP0nvxkOHoBAHQd46TxjRuEMPAVAlL4DvPS/gPZ+/F3z4AOBug/w0v8y2vvxd8rDDsTq7L2gN4S8A3PZRdybSLchvUAGI0zAdwOsqq4KN+ARBpIYJ8BL87W10rdTCIAZjLMFvWqzkdsgk3aki3NyA+QxYIDvqtar5Touw6WoL5DKUFvQj4ZZbV0YLsJjCGQzbICXaukKWcr1A2Aww25BPyq4ixseQjvSJ4Q/awC/jTwBr0qtvxnW8QzHAMB1U0zAqyvjY6rymYP3S/XEAaymmIBX59bihLdBZTseAI6aawJe7Zwgk3fOHLxH8icRmNZcE/Bqz1Br4Qagnkkn4N/u02R30TUEf9TdcwpM4iv6ALKwTAPQ0qRb0MNw3gDQKQEGgAACDAABBLh7dqG3uU8NyEmAASCAAI/AEAzQHQFmfHahgYS8Ecc4ZGabfQKOevk75YVEKd6IA+Af2+eyj/9fMeYKE/BQDMHbLJe8c/F3x0uLEwR4NN01+HHlanDwFkpWNV5vXmDsJ8ADyt/gPYtUvZ/CEjk553kkIcBjStvgEwuTMYVSWv5eeI3xkT9DGlPOX/5zR1XjZ0l7gkI9jZ90rzE+MgGPLNsScCWllX6WnGcqlBX7i+A1xjsCPLg8Db64DFX9QSyRo8rz+l+8zPjFFjR9qLp4pVqmKSXb0/q93LIdErFMwFPI8Gt/vaBuXmWnDC/4j7zYEOBZhC9JXQT4zsrYtfCX+lFeb9MS4Ln0fjeKBrOtu/quvOQmJMDTCVyhOgrwnTWxL/3W94kX3iTchDUdv9v7DbOgD2+w+5sG+3F4xwQ8qZBf7yLtb3/kTlkyGz5UXn4DMwFPym/1fsaRnCZ5Xmb4GaclwPNq32BLCUVMkt7VVD/sVAR4aj3OwVHHbBHMYLb0rub8qYcnwLNr3DPrCOdMm96VR2A8bsJiWTr8mLZ+byLjEMl5yUtxDALMXx29zUXUumzha0Z6P/Jq7J0taP76WW69/Er3cpycYK91J49S70zAvFb7d7vTIVj4q1KUc7wsO2UCplcWHbhz4tIpAea12nmzZPDES+IKj16PBJi3al8Svr5kGIJhpcHdEWA+0GDohQb3RYD5TIMbf6M5eXiL0OCOCDC7JF8cGxxe8kcAVhrcCwEmXpH1omog1Ze++FvqLggw46iUSfVtxkNdlgYnJ8DslfxK8F3xg5QEumYUzsw7YXFA1d/ksqnr6N4unghGJV7S2QgwB+R/f8pHp4/WOhVOg+vx8s7DFjQH9PWre+KNRDr6OAo4x8lNHgLM4HY2VXqZhwYnYQuaYzq6DPzb74MX3bREogGv/1gCzGFdN5heCHADft1i2YIG0lHfNjzOsQSYw5w1wzA0ONBX9AHAP77dDDWBp0X/6RmXBCbhGjAn1VslBXhU26+Z+/OuviH80oWwBU06luAhfXxavWkisxFgTnLKzH7KmpwnKIQAA3VZ3LvgaWpPgMnIWgAMT4A5zy40HzmX6ognqzEBBoAAAswl9YZgJ+MD8CR2x1PWkgADQAABBuAvQ3AzAsxVbsXiJes4bBNgAAggwBRgCIaR2L1oQ4ApQ4N5ZAWHjwQYAAIIMMUYggH2E2BKKthgOW/G5wDym5dEA1/RB8Bofqzm/Xh8ph7/74tnP14AsMefW/QRMKorq7Dxt4GdT9CJ50KAh+E3sSpb0NRy+lfX73wq9w3q/U1VX9jJBEwL+xdl9W2j0v6E+o7Hr2Q9rgHTwv13eHt19nveC5WFIkzAMB0F5RAnx5W4BgzAFmdslQgwAAQQYJiLaYYTvGxqEGAACCDAAHxmCC5OgAEggADDXPxJCSQhwAAQQIABIIAAA0AAAYbpuAwMGQgwAAQQYJiRIRjCCTAABBBgmJQhGGIJMMxLgyGQAMPUfpabDEMIAQZkGAJ8RR8AkMW9wXs+9OZdrX1gDuz35xZ9BMB4lHhUdkoKMgED5T0t03oMvwkwUN3LsUmVmZwAAzHWKisxc3IXNBDMPdjMSYABIIAAAykYgpmNAANAAAEGYBe7FGUJMJCF9Z2pCDAABBBgAAggwAB85gJBcQIMZOEtsZiKt6IEYsgtkxNgoCKVhXcEGKhCekfiAnANAgwUJr2whwADxUgv7OcuaKAM9YVDBBgoQH3hKAEGrlJfOEGAgUvUF84RYOA89YXTBBgAAggwcJLxdxLehaMSAQbOUF+4SICBw9QXrhNg4Bj1nYr953oEGAACCDAArxl/qxJgAAggwMABLgBDKQIMwAv2n2sTYAAIIMAAPDP+NvAVfQDAvH6v8q4xMw8BBg74WW7XG2m6Ss4T1IYtaKCdn+W2vbhb+pmHCRg45t7IQ3OwrHbEk9WMAANn7Myw1RzesQUNnLfR14+7zSTkKWvJBAxcYsmGc0zAABBAgAEggAADiXgjDuYhwAAQQIABIIAAA1nYf47lhvbGBBgAAggwAAQQYAAIIMBACi4AMxsBBsAdWAEEGGB26htCgAEggAADKRjConjkowgwAAQQYIB5GX8DCTAABBBgIAvTWHv+/DqQAANAAAEGEjEEN+YBDyTAQC6SwCQEGEhHg5txDTiQAAMZaTDDE2AgKQ1mbAIM5KXBDdiFjiLAQGoazKgEGMhOgxnSn1v0EQDsZLO0Buc3UUzAQDd+lptaMAwBBjojw4xBgIEuyTC9+4o+AIDzdjbYxWMSMgED4zMuk5AAA7PQ4N88JoEEGJiI3pCHAANzsR1NEgIMzEiDFw9CNAEGJiU/xBJgYF4aTCABBqamwUTxYQwAy/LpzTqeOj3AO3s48wgnwAAndZ1hAQ5nCxrgJA3jCgEGgAACDDAds3sGAgwAAQQY4KSub8IinAADzMX+cxICDHBGp+Ov+ubxFX0AALQgvdmYgAEO6278Vd+EBBjgGPWlCFvQAAf0VV/pzUyAAXbpK72L+qZnCxrgM/WlOBMwwAd91Vd6eyHAAFs6qq/09kWAAd7qor662ykBBuiS7vZOgAFeyzn+6u4wBBigD9I7GH+GBNAB9R2PAAO8kGr/WX2HJMAAqanvqAQYIC/1HZibsABS0NrZCDBAMOmdky1ogEjqOy0BBoAAAgwQxvg7MwEGgAACDPCC2ZTaBBgghsZPToABIIAAAwQw/iLAABDAO2EBNGX25e7PLfoIANIq+6GE0ssjEzBAddLLb64BA9SlvrwkwAAQQIABIIAAA0CA/wMijwbhSpVtFgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "import dinov2.eval.segmentation.utils.colormaps as colormaps\n", "\n", "\n", "DATASET_COLORMAPS = {\n", " \"ade20k\": colormaps.ADE20K_COLORMAP,\n", " \"voc2012\": colormaps.VOC2012_COLORMAP,\n", "}\n", "\n", "\n", "def render_segmentation(segmentation_logits, dataset):\n", " colormap = DATASET_COLORMAPS[dataset]\n", " colormap_array = np.array(colormap, dtype=np.uint8)\n", " segmentation_values = colormap_array[segmentation_logits + 1]\n", " return Image.fromarray(segmentation_values)\n", "\n", "\n", "array = np.array(image)[:, :, ::-1] # BGR\n", "segmentation_logits = inference_segmentor(model, array)[0]\n", "segmented_image = render_segmentation(segmentation_logits, HEAD_DATASET)\n", "display(segmented_image)" ] }, { "cell_type": "markdown", "id": "de40012e-a01e-4e73-bb71-3048f16d41c8", "metadata": {}, "source": [ "## Load pretrained segmentation model (Mask2Former)" ] }, { "cell_type": "code", "execution_count": 8, "id": "ff2cbbbe-c53c-4e5b-977f-c2a7d93f4b8c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/private/home/plabatut/github/patricklabatut/dinov2/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py:222: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.\n", " warnings.warn(\n", "/private/home/plabatut/.conda/envs/dinov2-extras-conda/lib/python3.9/site-packages/mmcv/ops/multi_scale_deform_attn.py:209: UserWarning: You'd better set embed_dims in MultiScaleDeformAttention to make the dimension of each attention head a power of 2 which is more efficient in our CUDA implementation.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "load checkpoint from http path: https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_ade20k_m2f.pth\n" ] }, { "data": { "text/plain": [ "EncoderDecoderMask2Former(\n", " (backbone): ViTAdapter(\n", " (patch_embed): PatchEmbed(\n", " (proj): Conv2d(3, 1536, kernel_size=(14, 14), stride=(14, 14))\n", " (norm): Identity()\n", " )\n", " (pos_drop): Dropout(p=0.0, inplace=False)\n", " (blocks): Sequential(\n", " (0): Block(\n", " (norm1): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (attn): Attention(\n", " (qkv): Linear(in_features=1536, out_features=4608, bias=True)\n", " (attn_drop): Dropout(p=0.0, inplace=False)\n", " (proj): Linear(in_features=1536, out_features=1536, bias=True)\n", " (proj_drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (drop_path): Identity()\n", " (norm2): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (mlp): SwiGLUFFN(\n", " (w1): Linear(in_features=1536, out_features=4096, bias=True)\n", " (w2): Linear(in_features=1536, out_features=4096, bias=True)\n", " (w3): Linear(in_features=4096, out_features=1536, bias=True)\n", " )\n", " )\n", " (1): Block(\n", " (norm1): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (attn): Attention(\n", " (qkv): Linear(in_features=1536, out_features=4608, bias=True)\n", " (attn_drop): Dropout(p=0.0, inplace=False)\n", " (proj): Linear(in_features=1536, out_features=1536, bias=True)\n", " (proj_drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (drop_path): DropPath()\n", " (norm2): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (mlp): SwiGLUFFN(\n", " (w1): Linear(in_features=1536, out_features=4096, bias=True)\n", " (w2): Linear(in_features=1536, out_features=4096, bias=True)\n", " (w3): Linear(in_features=4096, out_features=1536, bias=True)\n", " )\n", " )\n", " (2): Block(\n", " (norm1): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (attn): Attention(\n", " (qkv): Linear(in_features=1536, out_features=4608, bias=True)\n", " (attn_drop): 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Linear(in_features=1536, out_features=768, bias=True)\n", " (output_proj): Linear(in_features=768, out_features=1536, bias=True)\n", " )\n", " )\n", " (extractor): Extractor(\n", " (query_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (feat_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (attn): MSDeformAttn(\n", " (sampling_offsets): Linear(in_features=1536, out_features=192, bias=True)\n", " (attention_weights): Linear(in_features=1536, out_features=96, bias=True)\n", " (value_proj): Linear(in_features=1536, out_features=768, bias=True)\n", " (output_proj): Linear(in_features=768, out_features=1536, bias=True)\n", " )\n", " (ffn): ConvFFN(\n", " (fc1): Linear(in_features=1536, out_features=384, bias=True)\n", " (dwconv): DWConv(\n", " (dwconv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384)\n", " )\n", " (act): GELU(approximate='none')\n", " (fc2): Linear(in_features=384, out_features=1536, bias=True)\n", " (drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (ffn_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (drop_path): DropPath()\n", " )\n", " )\n", " (3): InteractionBlockWithCls(\n", " (injector): Injector(\n", " (query_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (feat_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (attn): MSDeformAttn(\n", " (sampling_offsets): Linear(in_features=1536, out_features=576, bias=True)\n", " (attention_weights): Linear(in_features=1536, out_features=288, bias=True)\n", " (value_proj): Linear(in_features=1536, out_features=768, bias=True)\n", " (output_proj): Linear(in_features=768, out_features=1536, bias=True)\n", " )\n", " )\n", " (extractor): Extractor(\n", " (query_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (feat_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (attn): MSDeformAttn(\n", " (sampling_offsets): Linear(in_features=1536, out_features=192, bias=True)\n", " (attention_weights): Linear(in_features=1536, out_features=96, bias=True)\n", " (value_proj): Linear(in_features=1536, out_features=768, bias=True)\n", " (output_proj): Linear(in_features=768, out_features=1536, bias=True)\n", " )\n", " (ffn): ConvFFN(\n", " (fc1): Linear(in_features=1536, out_features=384, bias=True)\n", " (dwconv): DWConv(\n", " (dwconv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384)\n", " )\n", " (act): GELU(approximate='none')\n", " (fc2): Linear(in_features=384, out_features=1536, bias=True)\n", " (drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (ffn_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (drop_path): DropPath()\n", " )\n", " (extra_extractors): Sequential(\n", " (0): Extractor(\n", " (query_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (feat_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (attn): MSDeformAttn(\n", " (sampling_offsets): Linear(in_features=1536, out_features=192, bias=True)\n", " (attention_weights): Linear(in_features=1536, out_features=96, bias=True)\n", " (value_proj): Linear(in_features=1536, out_features=768, bias=True)\n", " (output_proj): Linear(in_features=768, out_features=1536, bias=True)\n", " )\n", " (ffn): ConvFFN(\n", " (fc1): Linear(in_features=1536, out_features=384, bias=True)\n", " (dwconv): DWConv(\n", " (dwconv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384)\n", " )\n", " (act): GELU(approximate='none')\n", " (fc2): Linear(in_features=384, out_features=1536, bias=True)\n", " (drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (ffn_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (drop_path): DropPath()\n", " )\n", " (1): Extractor(\n", " (query_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (feat_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (attn): MSDeformAttn(\n", " (sampling_offsets): Linear(in_features=1536, out_features=192, bias=True)\n", " (attention_weights): Linear(in_features=1536, out_features=96, bias=True)\n", " (value_proj): Linear(in_features=1536, out_features=768, bias=True)\n", " (output_proj): Linear(in_features=768, out_features=1536, bias=True)\n", " )\n", " (ffn): ConvFFN(\n", " (fc1): Linear(in_features=1536, out_features=384, bias=True)\n", " (dwconv): DWConv(\n", " (dwconv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384)\n", " )\n", " (act): GELU(approximate='none')\n", " (fc2): Linear(in_features=384, out_features=1536, bias=True)\n", " (drop): Dropout(p=0.0, inplace=False)\n", " )\n", " (ffn_norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)\n", " (drop_path): DropPath()\n", " )\n", " )\n", " )\n", " )\n", " (up): ConvTranspose2d(1536, 1536, kernel_size=(2, 2), stride=(2, 2))\n", " (norm1): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (norm2): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (norm3): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " (norm4): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", " )\n", " (decode_head): Mask2FormerHead(\n", " input_transform=multiple_select, ignore_index=255, align_corners=False\n", " (loss_decode): CrossEntropyLoss(avg_non_ignore=False)\n", " (conv_seg): None\n", " (dropout): Dropout2d(p=0.1, inplace=False)\n", " (pixel_decoder): MSDeformAttnPixelDecoder(\n", " (input_convs): ModuleList(\n", " (0-2): 3 x ConvModule(\n", " (conv): Conv2d(1536, 1536, kernel_size=(1, 1), stride=(1, 1))\n", " (gn): GroupNorm(32, 1536, eps=1e-05, affine=True)\n", " )\n", " )\n", " (encoder): DetrTransformerEncoder(\n", " (layers): ModuleList(\n", " (0-5): 6 x BaseTransformerLayer(\n", " (attentions): ModuleList(\n", " (0): MultiScaleDeformableAttention(\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " (sampling_offsets): Linear(in_features=1536, out_features=768, bias=True)\n", " (attention_weights): Linear(in_features=1536, out_features=384, bias=True)\n", " (value_proj): Linear(in_features=1536, out_features=1536, bias=True)\n", " (output_proj): Linear(in_features=1536, out_features=1536, bias=True)\n", " )\n", " )\n", " (ffns): ModuleList(\n", " (0): FFN(\n", " (activate): ReLU(inplace=True)\n", " (layers): Sequential(\n", " (0): Sequential(\n", " (0): Linear(in_features=1536, out_features=6144, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.0, inplace=False)\n", " )\n", " (1): Linear(in_features=6144, out_features=1536, bias=True)\n", " (2): Dropout(p=0.0, inplace=False)\n", " )\n", " (dropout_layer): Identity()\n", " )\n", " )\n", " (norms): ModuleList(\n", " (0-1): 2 x LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " )\n", " )\n", " (postional_encoding): SinePositionalEncoding(num_feats=768, temperature=10000, normalize=True, scale=6.283185307179586, eps=1e-06)\n", " (level_encoding): Embedding(3, 1536)\n", " (lateral_convs): ModuleList(\n", " (0): ConvModule(\n", " (conv): Conv2d(1536, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (gn): GroupNorm(32, 1536, eps=1e-05, affine=True)\n", " )\n", " )\n", " (output_convs): ModuleList(\n", " (0): ConvModule(\n", " (conv): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " (gn): GroupNorm(32, 1536, eps=1e-05, affine=True)\n", " (activate): ReLU(inplace=True)\n", " )\n", " )\n", " (mask_feature): Conv2d(1536, 1536, kernel_size=(1, 1), stride=(1, 1))\n", " )\n", " (transformer_decoder): DetrTransformerDecoder(\n", " (layers): ModuleList(\n", " (0-8): 9 x DetrTransformerDecoderLayer(\n", " (attentions): ModuleList(\n", " (0-1): 2 x MultiheadAttention(\n", " (attn): MultiheadAttention(\n", " (out_proj): NonDynamicallyQuantizableLinear(in_features=1536, out_features=1536, bias=True)\n", " )\n", " (proj_drop): Dropout(p=0.0, inplace=False)\n", " (dropout_layer): Identity()\n", " )\n", " )\n", " (ffns): ModuleList(\n", " (0): FFN(\n", " (activate): ReLU(inplace=True)\n", " (layers): Sequential(\n", " (0): Sequential(\n", " (0): Linear(in_features=1536, out_features=6144, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Dropout(p=0.0, inplace=False)\n", " )\n", " (1): Linear(in_features=6144, out_features=1536, bias=True)\n", " (2): Dropout(p=0.0, inplace=False)\n", " )\n", " (dropout_layer): Identity()\n", " )\n", " )\n", " (norms): ModuleList(\n", " (0-2): 3 x LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " )\n", " (post_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (decoder_input_projs): ModuleList(\n", " (0-2): 3 x Identity()\n", " )\n", " (decoder_positional_encoding): SinePositionalEncoding(num_feats=768, temperature=10000, normalize=True, scale=6.283185307179586, eps=1e-06)\n", " (query_embed): Embedding(100, 1536)\n", " (query_feat): Embedding(100, 1536)\n", " (level_embed): Embedding(3, 1536)\n", " (cls_embed): Linear(in_features=1536, out_features=151, bias=True)\n", " (mask_embed): Sequential(\n", " (0): Linear(in_features=1536, out_features=1536, bias=True)\n", " (1): ReLU(inplace=True)\n", " (2): Linear(in_features=1536, out_features=1536, bias=True)\n", " (3): ReLU(inplace=True)\n", " (4): Linear(in_features=1536, out_features=1536, bias=True)\n", " )\n", " (loss_cls): CrossEntropyLoss(avg_non_ignore=False)\n", " (loss_mask): CrossEntropyLoss(avg_non_ignore=False)\n", " (loss_dice): DiceLoss()\n", " )\n", ")" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import dinov2.eval.segmentation_m2f.models.segmentors\n", "\n", "CONFIG_URL = f\"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f_config.py\"\n", "CHECKPOINT_URL = f\"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f.pth\"\n", "\n", "cfg_str = load_config_from_url(CONFIG_URL)\n", "cfg = mmcv.Config.fromstring(cfg_str, file_format=\".py\")\n", "\n", "model = init_segmentor(cfg)\n", "load_checkpoint(model, CHECKPOINT_URL, map_location=\"cpu\")\n", "model.cuda()\n", "model.eval()" ] }, { "cell_type": "markdown", "id": "53c0309f-df2b-4912-bca5-e57d8b3875b3", "metadata": {}, "source": [ "## Semantic segmentation on sample image" ] }, { "cell_type": "code", "execution_count": 9, "id": "f4abb13b-0e5a-4a40-8d44-21da4286ba7d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/private/home/plabatut/.conda/envs/dinov2-extras-conda/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1678402374358/work/aten/src/ATen/native/TensorShape.cpp:3483.)\n", " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "array = np.array(image)[:, :, ::-1] # BGR\n", "segmentation_logits = inference_segmentor(model, array)[0]\n", "segmented_image = render_segmentation(segmentation_logits, \"ade20k\")\n", "display(segmented_image)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.17" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: pyproject.toml ================================================ [tool.black] line-length = 120 [tool.pylint.master] persistent = false score = false [tool.pylint.messages_control] disable = "all" enable = [ "miscellaneous", "similarities", ] [tool.pylint.similarities] ignore-comments = true ignore-docstrings = true ignore-imports = true min-similarity-lines = 8 [tool.pylint.reports] reports = false [tool.pylint.miscellaneous] notes = [ "FIXME", "XXX", "TODO", ] ================================================ FILE: requirements-dev.txt ================================================ black==22.6.0 flake8==5.0.4 pylint==2.15.0 ================================================ FILE: requirements-extras.txt ================================================ mmcv-full==1.5.0 mmsegmentation==0.27.0 ================================================ FILE: requirements.txt ================================================ --extra-index-url https://download.pytorch.org/whl/cu117 torch==2.0.0 torchvision==0.15.0 omegaconf torchmetrics==0.10.3 fvcore iopath xformers==0.0.18 submitit --extra-index-url https://pypi.nvidia.com cuml-cu11 ================================================ FILE: scripts/cell_dino/launcher_knn_eval_on_chammi.sh ================================================ #!/bin/bash # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # 1 : modify CHANNEL_AGNOSTIC_CELL_MODEL, CHAMMI_DATA_PATH and OUTPUT_DIR below # 2 : call this script with the two arguments specified below #Arguments: # $1 : dataset, e.g CP # $2 : task number, e.g TASK_TWO CHAMMI_DATA_PATH="" CHANNEL_AGNOSTIC_CELL_MODEL="path_to_model/model.pth" OUTPUT_DIR=YOUR_OUTPUT_PATH_$1_$2 if [ "$2" == "TASK_FOUR" ]; then OTHER_ARG="--leave-one-out-dataset $CHAMMI_DATA_PATH/CP/enriched_meta.csv" elif [ "$1" == "HPA" -a "$2" == "TASK_THREE" ]; then OTHER_ARG="--leave-one-out-dataset $CHAMMI_DATA_PATH/CHAMMI/HPA/enriched_meta.csv" else OTHER_ARG="" fi echo $OTHER_ARG PYTHONPATH=..:../../dinov2/data python ../../dinov2/run/eval/cell_dino/knn.py \ --config-file ../../dinov2/configs/eval/cell_dino/vitl16_channel_adaptive_pretrain.yaml \ --pretrained-weights $CHANNEL_AGNOSTIC_CELL_MODEL \ --output-dir $OUTPUT_DIR \ --train-dataset CHAMMI_$1:split=TRAIN:root=$CHAMMI_DATA_PATH \ --val-dataset CHAMMI_$1:split=$2:root=$CHAMMI_DATA_PATH \ --metric-type mean_per_class_multiclass_f1 \ --crop-size 224 \ --batch-size 32 \ --resize-size 256 \ --bag-of-channels \ $OTHER_ARG \ ================================================ FILE: scripts/cell_dino/launcher_linear_eval_on_chammi.sh ================================================ #!/bin/bash # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. # 1 : modify CHANNEL_AGNOSTIC_CELL_MODEL, CHAMMI_DATA_PATH and OUTPUT_DIR below # 2 : call this script with the two arguments specified below #Arguments: # $1 : dataset, e.g CP # $2 : task number, e.g TASK_TWO CHAMMI_DATA_PATH="" CHANNEL_AGNOSTIC_CELL_MODEL="path_to_model/model.pth" OUTPUTDIR=YOUR_OUTPUT_PATH_$1_$2 if [ "$2" == "TASK_FOUR" ]; then OTHER_ARG="--leave-one-out-dataset $CHAMMI_DATA_PATH/CP/enriched_meta.csv" elif [ "$1" == "HPA" -a "$2" == "TASK_THREE" ]; then OTHER_ARG="--leave-one-out-dataset $CHAMMI_DATA_PATH/HPA/enriched_meta.csv" else OTHER_ARG="" fi if [ $1 != "CP" ]; then OTHER_ARG="$OTHER_ARG --resize-size 256" fi PYTHONPATH=..:../../dinov2/data python ../../dinov2/run/eval/cell_dino/linear.py \ --config-file ../../dinov2/configs/eval/cell_dino/vitl16_channel_adaptive_pretrain.yaml \ --pretrained-weights $CHANNEL_AGNOSTIC_CELL_MODEL \ --output-dir $OUTPUTDIR \ --train-dataset CHAMMI_$1:split=TRAIN:root=$CHAMMI_DATA_PATH \ --val-dataset CHAMMI_$1:split=$2:root=$CHAMMI_DATA_PATH \ --val-metric-type mean_per_class_multiclass_f1 \ --bag-of-channels \ --crop-size 224 \ --n-last-blocks 1 \ --avgpool \ --batch-size 128 \ --epoch-length 30 \ --epochs 10 \ $OTHER_ARG \ ================================================ FILE: scripts/lint.sh ================================================ #!/bin/sh if [ -n "$1" ]; then echo "linting \"$1\"" fi echo "running black" if [ -n "$1" ]; then black "$1" else black dinov2 fi echo "running flake8" if [ -n "$1" ]; then flake8 "$1" else flake8 fi echo "running pylint" if [ -n "$1" ]; then pylint "$1" else pylint dinov2 fi exit 0 ================================================ FILE: setup.cfg ================================================ [flake8] max-line-length = 120 ignore = E203,E501,W503 per-file-ignores = __init__.py:F401 hubconf.py:F401 exclude = venv ================================================ FILE: setup.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the Apache License, Version 2.0 # found in the LICENSE file in the root directory of this source tree. from pathlib import Path import re from typing import List, Tuple from setuptools import setup, find_packages NAME = "dinov2" DESCRIPTION = "PyTorch code and models for the DINOv2 self-supervised learning method." URL = "https://github.com/facebookresearch/dinov2" AUTHOR = "FAIR" REQUIRES_PYTHON = ">=3.9.0" HERE = Path(__file__).parent try: with open(HERE / "README.md", encoding="utf-8") as f: long_description = "\n" + f.read() except FileNotFoundError: long_description = DESCRIPTION def get_requirements(path: str = HERE / "requirements.txt") -> Tuple[List[str], List[str]]: requirements = [] extra_indices = [] with open(path) as f: for line in f.readlines(): line = line.rstrip("\r\n") if line.startswith("--extra-index-url "): extra_indices.append(line[18:]) continue requirements.append(line) return requirements, extra_indices def get_package_version() -> str: with open(HERE / "dinov2/__init__.py") as f: result = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", f.read(), re.M) if result: return result.group(1) raise RuntimeError("Can't get package version") requirements, extra_indices = get_requirements() version = get_package_version() dev_requirements, _ = get_requirements(HERE / "requirements-dev.txt") extras_requirements, _ = get_requirements(HERE / "requirements-extras.txt") setup( name=NAME, version=version, description=DESCRIPTION, long_description=long_description, long_description_content_type="text/markdown", author=AUTHOR, python_requires=REQUIRES_PYTHON, url=URL, packages=find_packages(), package_data={ "": ["*.yaml"], }, install_requires=requirements, extras_require={ "dev": dev_requirements, "extras": extras_requirements, }, dependency_links=extra_indices, install_package_data=True, license="Apache", license_files=("LICENSE",), classifiers=[ # Trove classifiers: https://github.com/pypa/trove-classifiers/blob/main/src/trove_classifiers/__init__.py "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3.9", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development :: Libraries :: Python Modules", ], )