[
{
"path": ".gitignore",
"content": "logs/\nwandb/\nmodels/\nfeatures/\nresults/\ntmp/\n\ntests/data/\n*.pt\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\nsync.sh\ngpu1sync.sh\n.idea\n*.pdf\n**/._*\n**/*DS_*\n**.jsonl\nsrc/sbatch\nsrc/misc\n.vscode\nsrc/debug\ncore.*\n\n# Allow\n!src/evaluation/misc/results_dbs/*\nowens-venv/\npitzer-venv/\n\n# visualiziations\ndata/visuals/\n\n# Other\ncontainers\ndata/TreeOfLife-10M\n"
},
{
"path": "CITATION.cff",
"content": "---\nabstract: \"Images of the natural world are an abundant source of biological\n information. There are many computational methods and tools, particularly\n computer vision, for extracting information from images. However, existing\n methods consist of bespoke models, not adaptable or extendable from their\n targeted task to new questions, contexts, and datasets. We thus develop the\n first large-scale multimodal model, BioCLIP, for general biology questions on\n images. We leverage the unique properties of biology (abundance and variety of\n images and availability of rich structured biological knowledge) as the the\n application domain for computer vision.\"\nauthors:\n - family-names: Stevens\n given-names: Samuel\n - family-names: Wu\n given-names: Jiaman\n - family-names: Thompson\n given-names: \"Matthew J.\"\n - family-names: Campolongo\n given-names: \"Elizabeth G.\"\n - family-names: Song\n given-names: \"Chan Hee\"\n - family-names: Carlyn\n given-names: \"David Edward\"\ncff-version: 1.2.0\ndate-released: \"2024-09-19\"\nidentifiers:\n - doi: \"10.5281/zenodo.10895870\"\n - description: \"The GitHub release URL of tag v1.0.2.\"\n type: url\n value: \"https://github.com/Imageomics/bioclip/releases/tag/v1.0.2\"\n - description: \"The GitHub URL of the commit tagged with v1.0.2.\"\n type: url\n value: \"https://github.com/Imageomics/bioclip/tree/b750fa8758d16e78ccc3de7ab86c1523d5de6148\"\nkeywords:\n - clip\n - biology\n - CV\n - imageomics\n - animals\n - species\n - images\n - taxonomy\n - \"rare species\"\n - \"endangered species\"\n - \"evolutionary biology\"\n - multimodal\n - \"knowledge-guided\"\nlicense: MIT\nmessage: \"If you use this software, please cite both the article from preferred-citation and the software itself.\"\nrepository-code: \"https://github.com/Imageomics/bioclip\"\ntitle: BioCLIP\nversion: 1.0.2\ntype: software\npreferred-citation:\n type: conference-paper\n authors:\n - family-names: Stevens\n given-names: Samuel\n - family-names: Wu\n given-names: Jiaman\n - family-names: Thompson\n given-names: \"Matthew J.\"\n - family-names: Campolongo\n given-names: \"Elizabeth G.\"\n - family-names: Song\n given-names: \"Chan Hee\"\n - family-names: Carlyn\n given-names: \"David Edward\"\n - family-names: Dong\n given-names: Li\n - family-names: Dahdul\n given-names: \"Wasila M\"\n - family-names: Stewart\n given-names: Charles\n - family-names: \"Berger-Wolf\"\n given-names: Tanya\n - family-names: Chao\n given-names: \"Wei-Lun\"\n - family-names: Su\n given-names: Yu\n title: \"BioCLIP: A Vision Foundation Model for the Tree of Life\"\n year: 2024\n pages: \"19412-19424\"\n collection-title: \"Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)\"\nreferences:\n - authors:\n - family-names: Ilharco\n given-names: Gabriel\n - family-names: Wortsman\n given-names: Mitchell\n - family-names: Wightman\n given-names: Ross\n - family-names: Gordon\n given-names: Cade\n - family-names: Carlini\n given-names: Nicholas\n - family-names: Taori\n given-names: Rohan\n - family-names: Dave\n given-names: Achal\n - family-names: Shankar\n given-names: Vaishaal\n - family-names: Namkoong\n given-names: Hongseok\n - family-names: Miller\n given-names: John\n - family-names: Hajishirzi\n given-names: Hannaneh\n - family-names: Farhadi\n given-names: Ali\n - family-names: Schmidt\n given-names: Ludwig\n title: OpenCLIP\n version: v0.1\n type: software\n doi: \"10.5281/zenodo.5143773\"\n date-released: \"2021-07-28\"\n"
},
{
"path": "HISTORY.md",
"content": "## 2.14.0\n\n* Move dataset mixtures logic to shard level\n* Fix CoCa accum-grad training\n* Safer transformers import guard\n* get_labels refactoring\n\n## 2.13.0\n\n* Add support for dataset mixtures with different sampling weights\n* Make transformers optional again \n\n## 2.12.0\n\n* Updated convnext configs for consistency\n* Added input_patchnorm option\n* Clean and improve CoCa generation\n* Support model distillation\n* Add ConvNeXt-Large 320x320 fine-tune weights\n\n## 2.11.1\n\n* Make transformers optional\n* Add MSCOCO CoCa finetunes to pretrained models\n\n## 2.11.0\n\n* coca support and weights\n* ConvNeXt-Large weights\n\n## 2.10.1\n\n* `hf-hub:org/model_id` support for loading models w/ config and weights in Hugging Face Hub\n\n## 2.10.0\n\n* Added a ViT-bigG-14 model.\n* Added an up-to-date example slurm script for large training jobs.\n* Added a option to sync logs and checkpoints to S3 during training.\n* New options for LR schedulers, constant and constant with cooldown\n* Fix wandb autoresuming when resume is not set\n* ConvNeXt `base` & `base_w` pretrained models added\n* `timm-` model prefix removed from configs\n* `timm` augmentation + regularization (dropout / drop-path) supported\n\n## 2.9.3\n\n* Fix wandb collapsing multiple parallel runs into a single one\n\n## 2.9.2\n\n* Fix braceexpand memory explosion for complex webdataset urls\n\n## 2.9.1\n\n* Fix release\n\n## 2.9.0\n\n* Add training feature to auto-resume from the latest checkpoint on restart via `--resume latest`\n* Allow webp in webdataset\n* Fix logging for number of samples when using gradient accumulation\n* Add model configs for convnext xxlarge\n\n## 2.8.2\n\n* wrapped patchdropout in a torch.nn.Module\n\n## 2.8.1\n\n* relax protobuf dependency\n* override the default patch dropout value in 'vision_cfg'\n\n## 2.8.0\n\n* better support for HF models\n* add support for gradient accumulation\n* CI fixes\n* add support for patch dropout\n* add convnext configs\n\n\n## 2.7.0\n\n* add multilingual H/14 xlm roberta large\n\n## 2.6.1\n\n* fix setup.py _read_reqs\n\n## 2.6.0\n\n* Make openclip training usable from pypi.\n* Add xlm roberta large vit h 14 config.\n\n## 2.5.0\n\n* pretrained B/32 xlm roberta base: first multilingual clip trained on laion5B\n* pretrained B/32 roberta base: first clip trained using an HF text encoder\n\n## 2.4.1\n\n* Add missing hf_tokenizer_name in CLIPTextCfg.\n\n## 2.4.0\n\n* Fix #211, missing RN50x64 config. Fix type of dropout param for ResNet models\n* Bring back LayerNorm impl that casts to input for non bf16/fp16 \n* zero_shot.py: set correct tokenizer based on args\n* training/params.py: remove hf params and get them from model config\n\n## 2.3.1\n\n* Implement grad checkpointing for hf model.\n* custom_text: True if hf_model_name is set\n* Disable hf tokenizer parallelism \n\n## 2.3.0\n\n* Generalizable Text Transformer with HuggingFace Models (@iejMac)\n\n## 2.2.0\n\n* Support for custom text tower\n* Add checksum verification for pretrained model weights \n\n## 2.1.0\n\n* lot including sota models, bfloat16 option, better loading, better metrics\n\n## 1.2.0\n\n* ViT-B/32 trained on Laion2B-en\n* add missing openai RN50x64 model\n\n## 1.1.1\n\n* ViT-B/16+\n* Add grad checkpointing support\n* more robust data loader\n"
},
{
"path": "LICENSE",
"content": "Copyright (c) 2023-2024 Imageomics Institute\n\nCopyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, \nNicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, \nJohn Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, \nLudwig Schmidt\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\nNONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE\nLIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION\nOF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION\nWITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n"
},
{
"path": "MANIFEST.in",
"content": "include src/open_clip/bpe_simple_vocab_16e6.txt.gz\ninclude src/open_clip/model_configs/*.json\n\n"
},
{
"path": "Makefile",
"content": "install: ## [Local development] Upgrade pip, install requirements, install package.\n\tpython -m pip install -U pip\n\tpython -m pip install -e .\n\ninstall-training:\n\tpython -m pip install -r requirements-training.txt\n\nlint:\n\tfind src/evaluation -iname '*.py' | xargs ruff check\n"
},
{
"path": "README.md",
"content": "# BioCLIP [](https://zenodo.org/doi/10.5281/zenodo.10895870)\n\n\nThis is the repository for the [BioCLIP model](https://huggingface.co/imageomics/bioclip) and the [TreeOfLife-10M dataset](https://huggingface.co/datasets/imageomics/TreeOfLife-10M). It contains the code used for training and the evaluation of BioCLIP (testing and visualizing embeddings). Additionally, we include a collection of scripts for forming, evaluating, and visualizing the data used for TreeOfLife-10M and the [Rare Species benchmark](https://huggingface.co/datasets/imageomics/rare-species) we created alongside it. The BioCLIP website is hosted from the `gh-pages` branch of this repository.\n\n[Paper](https://arxiv.org/abs/2311.18803) | [Model](https://huggingface.co/imageomics/bioclip) | [Data](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) | [Demo](https://huggingface.co/spaces/imageomics/bioclip-demo)\n---\n\nBioCLIP is a CLIP model trained on a new 10M-image dataset of biological organisms with fine-grained taxonomic labels.\nBioCLIP outperforms general domain baselines on a wide spread of biology-related tasks, including zero-shot and few-shot classification.\n\n## Table of Contents\n\n1. [Model](#model)\n2. [Data](#data)\n3. [Paper, website, and docs](#paper)\n4. [Citation](#citation)\n\n## Model\n\nThe BioCLIP model is a ViT-B/16 pre-trained with the CLIP objective.\nBoth the ViT and the (small) autoregressive text encoder are available to download on [Hugging Face](https://huggingface.co/imageomics/bioclip).\n\nThe only dependency is the [`open_clip`](https://github.com/mlfoundations/open_clip) package.\n\nSee the [`examples/`](https://huggingface.co/imageomics/bioclip/tree/main/examples) directory on the [Hugging Face model repo](https://huggingface.co/imageomics/bioclip) for an example implementation.\nYou can also use the [pybioclip](https://github.com/Imageomics/pybioclip) package or the [BioCLIP demo](https://huggingface.co/spaces/imageomics/bioclip-demo) on Hugging Face.\n\n## Data\n\nBioCLIP was trained on TreeOfLife-10M (ToL-10M).\nThe data is a combination of [iNat21](https://github.com/visipedia/inat_comp/tree/master/2021), [BIOSCAN-1M](https://github.com/zahrag/BIOSCAN-1M), and data we collected and cleaned from [Encyclopedia of Life (EOL)](https://eol.org). It contains images for more than 450K distinct taxa, as measured by 7-rank [Linnaean taxonomy](https://www.britannica.com/science/taxonomy/The-objectives-of-biological-classification) (kingdom through species); this taxonomic string is associated to each image along with its common (or vernacular name) where available.\n\nWe cannot re-release the iNat21 or the BIOSCAN-1M datasets; however, we have uploaded our cleaned EOL data to [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) on Hugging Face.\nAfter downloading iNat21 and BIOSCAN-1M, the three datasets can be combined into TreeOfLife-10M in the [webdataset format](https://github.com/webdataset/webdataset) for model training by following the directions in [`treeoflife10m.md`](/docs/imageomics/treeoflife10m.md).\n\n\n10 biologically-relevant datasets were used for various tests of [BioCLIP](https://huggingface.co/imageomics/bioclip), they are described (briefly) and linked to below. For more information about the contents of these datasets, see Table 2 and associated sections of [our paper](https://doi.org/10.48550/arXiv.2311.18803). Annotations used alongside the datasets for evaluation are provided in subfolders of the `data/` directory named for the associated dataset.\n\n#### Test Sets\n\n- [Meta-Album](https://paperswithcode.com/dataset/meta-album): Specifically, we used the Plankton, Insects, Insects 2, PlantNet, Fungi, PlantVillage, Medicinal Leaf, and PlantDoc datasets from Set-0 through Set-2 (Set-3 had not yet been released).\n - [Birds 525](https://www.kaggle.com/datasets/gpiosenka/100-bird-species): We evaluated on the 2,625 test images provided with the dataset.\n - [Rare Species](https://huggingface.co/datasets/imageomics/rare-species): A new dataset we curated for the purpose of testing this model and to contribute to the ML for Conservation community. It consists of nearly 12K images representing 400 species labeled Near Threatened through Extinct in the Wild by the [IUCN Red List](https://www.iucnredlist.org/). For more information, see our [Rare Species dataset](https://huggingface.co/datasets/imageomics/rare-species).\n\n\n\n
\n\n\n### Summary\n\nBioCLIP outperforms general-domain baselines by 18% on average.\n\n### Model Examination\n\nWe encourage readers to see Section 4.6 of [our paper](https://doi.org/10.48550/arXiv.2311.18803).\nIn short, BioCLIP forms representations that more closely align to the taxonomic hierarchy compared to general-domain baselines like CLIP or OpenCLIP.\n\n\n## Citation \n\n**BibTeX:**\n\n```\n@software{bioclip2023,\n author = {Samuel Stevens and Jiaman Wu and Matthew J. Thompson and Elizabeth G. Campolongo and Chan Hee Song and David Edward Carlyn and Li Dong and Wasila M. Dahdul and Charles Stewart and Tanya Berger-Wolf and Wei-Lun Chao and Yu Su},\n doi = {10.57967/hf/1511},\n month = nov,\n title = {BioCLIP},\n version = {v0.1},\n year = {2023}\n}\n```\n\nPlease also cite our paper:\n\n```\n@article{stevens2023bioclip,\n title = {BIOCLIP: A Vision Foundation Model for the Tree of Life}, \n author = {Samuel Stevens and Jiaman Wu and Matthew J Thompson and Elizabeth G Campolongo and Chan Hee Song and David Edward Carlyn and Li Dong and Wasila M Dahdul and Charles Stewart and Tanya Berger-Wolf and Wei-Lun Chao and Yu Su},\n year = {2023},\n eprint = {2311.18803},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}\n}\n\n```\n\n\nPlease also consider citing OpenCLIP, iNat21 and BIOSCAN-1M:\n```\n@software{ilharco_gabriel_2021_5143773,\n author={Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig},\n title={OpenCLIP},\n year={2021},\n doi={10.5281/zenodo.5143773},\n}\n```\n```\n@misc{inat2021,\n author={Van Horn, Grant and Mac Aodha, Oisin},\n title={iNat Challenge 2021 - FGVC8},\n publisher={Kaggle},\n year={2021},\n url={https://kaggle.com/competitions/inaturalist-2021}\n}\n```\n```\n@inproceedings{gharaee2023step,\n author={Gharaee, Z. and Gong, Z. and Pellegrino, N. and Zarubiieva, I. and Haurum, J. B. and Lowe, S. C. and McKeown, J. T. A. and Ho, C. Y. and McLeod, J. and Wei, Y. C. and Agda, J. and Ratnasingham, S. and Steinke, D. and Chang, A. X. and Taylor, G. W. and Fieguth, P.},\n title={A Step Towards Worldwide Biodiversity Assessment: The {BIOSCAN-1M} Insect Dataset},\n booktitle={Advances in Neural Information Processing Systems ({NeurIPS}) Datasets \\& Benchmarks Track},\n year={2023},\n}\n```\n\n## Acknowledgements\n\nThe authors would like to thank Josef Uyeda, Jim Balhoff, Dan Rubenstein, Hank Bart, Hilmar Lapp, Sara Beery, and colleagues from the Imageomics Institute and the OSU NLP group for their valuable feedback. We also thank the BIOSCAN-1M team and the iNaturalist team for making their data available and easy to use, and Jennifer Hammack at EOL for her invaluable help in accessing EOL’s images.\n\nThe [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.\n\n\n## Model Card Authors\n\nElizabeth G. Campolongo, Samuel Stevens, and Jiaman Wu\n\n## Model Card Contact\n\n[stevens.994@osu.edu](mailto:stevens.994@osu.edu)\n"
},
{
"path": "docs/imageomics/RareSpecies_DatasetCard.md",
"content": "---\nLicense: cc0-1.0\nlanguage:\n- en\n- la\npretty_name: Rare Species Dataset\ntask_categories:\n- image-classification\n- zero-shot-classification\ntags:\n- biology\n- image\n- animals\n- species\n- taxonomy\n- rare species\n- endangered species\n- evolutionary biology\n- balanced\n- CV\n- multimodal\n- CLIP\n- knowledge-guided\nsize_categories: 10K\n- **Repository:** [Imageomics/bioclip](https://github.com/Imageomics/bioclip)\n- **Paper:** BioCLIP: A Vision Foundation Model for the Tree of Life ([arXiv](https://doi.org/10.48550/arXiv.2311.18803))\n\n\n### Dataset Summary\n\nThis dataset was generated alongside [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M); data (images and text) were pulled from [Encyclopedia of Life (EOL)](https://eol.org) to generate a dataset consisting of rare species for zero-shot-classification and more refined image classification tasks. Here, we use \"rare species\" to mean species listed on [The International Union for Conservation of Nature (IUCN) Red List](https://www.iucnredlist.org/) as Near Threatened, Vulnerable, Endangered, Critically Endangered, and Extinct in the Wild. \n\n\n\n\n\n||\n|:--|\n|**Figure 1.** Treemap from phyla down to family for Rare Species dataset. Interactive version available in [`visuals`](https://huggingface.co/imageomics/rare-species/tree/main/visuals) folder.|\n\n\n### Supported Tasks and Leaderboards\n\nImage Classification, Zero-shot and few-shot Classification.\n\nBaseline for Random guessing is 0.3.\n\n\n| Model | | Rare Species Classification Results | |\n| ---- | :----: | :----: | :----: | \n| | _Zero-Shot Classification_ | _One-Shot Classification_ | _Five-Shot Classification_ |\n| CLIP | 31.81 | 28.52 | 46.07 |\n| OpenCLIP | 29.85 | 29.26 | 47.45 | \n| BioCLIP | **38.09** | **44.9** | **65.7** |\n| --iNat21 Only | 21.33 | 36.94 | 55.65 |\n\n| |\n| -- |\n| Zero-, one- and five-shot classification top-1 accuracy for different CLIP models. **Bold** indicates best accuracy. All models use the same architecture: ViT-B/16 vision encoders, 77-token text encoder. \"iNat21 Only\" follows the same procedure as BioCLIP but uses iNat21 instead of TreeOfLife-10M. CLIP and OpenCLIP are tested on common name, while BioCLIP and iNat21 Only were tested on full taxonomic name + common name. In this manner, we compare the optimal CLIP and OpenCLIP performance (both were primarily trained with common names). |\n\n### Languages\nEnglish, Latin\n\n## Dataset Structure\n\n```\n/dataset/\n /\n __eol_full-size-copy.jpg\n __eol_full-size-copy.jpg\n ...\n __eol_full-size-copy.jpg\n /\n __eol_full-size-copy.jpg\n __eol_full-size-copy.jpg\n ...\n __eol_full-size-copy.jpg\n ...\n /\n __eol_full-size-copy.jpg\n __eol_full-size-copy.jpg\n ...\n __eol_full-size-copy.jpg\n metadata/\n rarespecies-catalog.csv\n licenses.csv\n visuals/\n phyla_ToL_tree.html\n phyla_ToL_tree.pdf\n phyla_ToL_tree.png\n```\n\n### Data Instances\n\nThis dataset is a collection of images with associated text. The text matched to images contains both [Linnaean taxonomy](https://www.britannica.com/science/taxonomy/The-objectives-of-biological-classification) (kingdom through species) for the particular subject of the image and its scientific name (``). All images have full 7-rank taxonomy filled, and are included in the [IUCN Red List](https://www.iucnredlist.org/) categories Near Threatened, Vulnerable, Endangered, Critically Endangered, and Extinct in the Wild. There are 30 images per species for the 400 species included.*\n\nThe images in this dataset are JPGs with filenames `__eol_full-size-copy.jpg`. See Metadata Files below for definition of the IDs.\n\n*It was discovered after training on TreeOfLife-10M that of the 400 species held out, 5 did not actually have 30 unique images, despite each image having unique EOL content IDs and EOL full-size image URLs. These species are as follows:\n| Species | Number of Unique Images |\n| --- | -- |\n| _Pheidole elecebra_ | 21 |\n| _Calumma ambreense_ | 27 |\n| _Acanthochelys macrocephala_ | 27 |\n| _Haliaeetus vociferoides_ | 29 |\n| _Wallago attu_ | 29 |\n\n\n### Data Fields\n\n#### Metadata Files\n\n`rarespecies-catalog.csv`: contains the following metadata associated with each image in the dataset\n - `rarespecies_id`: unique identifier for the image in the dataset.\n - `eol_content_id`: unique identifier within EOL database for images sourced from [EOL](https://eol.org). Note that EOL content IDs are not stable.\n - `eol_page_id`: identifier of page from which images from EOL are sourced. Note that an image's association to a particular page ID may change with updates to the EOL (or image provider's) hierarchy. However, EOL taxon page IDs are stable.\n The remaining terms describe the Linnaean taxonomy of the subject of the images; application of these labels is described below in the [annotation process](#annotation-process).\n - `kingdom`: kingdom to which the subject of the image belongs (all `Animalia`).\n - `phylum`: phylum to which the subject of the image belongs.\n - `class`: class to which the subject of the image belongs.\n - `order`: order to which the subject of the image belongs.\n - `family`: family to which the subject of the image belongs.\n - `genus`: genus to which the subject of the image belongs.\n - `species`: species to which the subject of the image belongs.\n - `sciName`: scientific name associated with the subject of the image (`genus-species`).\n - `common`: common name associated with the subject of the image. Note that there are only 398 unique common names; it is not uncommon for species of the same genera to share a common name. The two specific instances are _Acropora acuminata_ and _Acropora millepora_, which share the common name staghorn coral, and both _Tylototriton shanjing_ and _Tylototriton verrucosus_ have the common name Yunnan Newt.\n\n`licenses.csv`: File with license, source, and copyright holder associated to each image listed in `rarespecies-catalog.csv`; `rarespecies_id` is the shared unique identifier to link the two files. Columns are\n - `rarespecies_id`, `eol_content_id`, and `eol_page_id` are as defined above.\n - `md5`: MD5 hash of the image.\n - `medium_source_url`: URL pointing to source of image.\n - `eol_full_size_copy_url`: URL to access the full-sized image; this is the URL from which the image was downloaded for this dataset (see [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) for more information on this process).\n - `license_name`: name of license attached to the image (eg., `cc-by`).\n - `copyright_owner`: copyright holder for the image, filled with `not provided` if no copyright owner was provided. \n - `license_link`: URL to the listed license, left null in the case that `License Name` is `No known copyright restrictions`.\n - `title`: title provided for the image, filled with `not provided` if no title was provided.\n\n\n\nThe visuals folder has treemaps that were generated by feeding `rarespecies-catalog.csv` to the `taxa_viz` script in the [BioCLIP GitHub repository](https://github.com/Imageomics/bioclip).\n\n\n### Data Splits\n\nThis entire dataset was used for testing the [BioCLIP model](https://huggingface.co/imageomics/bioclip), which was trained on [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M).\n\n## Dataset Creation\n\n### Curation Rationale\n\nThis dataset was generated with the purpose of providing a biologically meaningful test set for the [Imageomics BioCLIP model](https://huggingface.co/imageomics/bioclip) to demonstrate robustness on data with minimal training samples available and biologically meaningful potential applications.\n\n### Source Data\n\n[EOL](https://eol.org) and [IUCN Red List](https://www.iucnredlist.org/)\n\n#### Initial Data Collection and Normalization\n\nThe IUCN Red List of Threatened Species categorization of animals was pulled from the [IUCN website](https://www.iucnredlist.org/). There are approximately 25,000 species that fall into the categories Near Threatened, Vulnerable, Endangered, Critically Endangered, and Extinct in the Wild (as of July 13, 2023), though image availability on EOL is not consistent across species. We select 400 species from the list under the condition there are at least 30 images per species available and they are not species in [iNat21](https://kaggle.com/competitions/inaturalist-2021) or [BIOSCAN-1M](https://zenodo.org/doi/10.5281/zenodo.8030064) datasets which were also used to generate [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M). A random subset of 30 images is then selected for each species in this collection.\n\nThis dataset was generated concurrently with [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M), so the process is as described [there](https://huggingface.co/datasets/imageomics/TreeOfLife-10M#initial-data-collection-and-normalization), with the exception that these images were entirely sourced from EOL, and the species represented were excluded from the TreeOfLife-10M dataset.\n\nThe IUCN data was used for selection of the included species, and is not reproduced here. [This link](https://www.iucnredlist.org/search?permalink=ab8daad6-d564-4370-b8e6-9c5ac9f8336f) provides the search used to gather the list of species classified as Near Threatened to Extinct in the Wild. The results were downloaded on July 13, 2023, but note the results are subject to change with IUCN Red List Updates ([IUCN Update Schedule](https://www.iucnredlist.org/assessment/updates)).\n\n\n### Annotations\n\n#### Annotation process\n\nAnnotations were primarily sourced from EOL (image source provider) following the procedure described in the [TreeOfLife-10M annotation process](https://huggingface.co/datasets/imageomics/TreeOfLife-10M#annotation-process). [IUCN Red List](https://www.iucnredlist.org/) was then used for filtering these taxa out of [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) to create this Rare Species dataset.\n\nThe scientific name (`genus-species`, as labeled by EOL) was used to look up the higher-order taxa from EOL aggregate datasets (described below), then matched against the ITIS hierarchy for the higher-order taxa standardization. A small number of these are [homonyms](https://en.wikipedia.org/wiki/Homonym_(biology)), for which a list was generated to ensure proper matching of higher-order taxa. After these resources were exhausted, any remaining unresolved taxa were fed through the [Global Names Resolver (GNR) API](https://resolver.globalnames.org/api). \n\n#### Who are the annotators?\n\nSamuel Stevens, Jiaman Wu, Matthew J. Thompson, and Elizabeth G. Campolongo\n\n### Personal and Sensitive Information\n\nAll animals included in this dataset are listed as Near Threatened, Vulnerable, Endangered, Critically Endangered, or Extinct in the Wild by the [IUCN Red List](https://www.iucnredlist.org/) as of July 13, 2023. (IUCN generally updates classifications twice each year; see the [IUCN Update Schedule](https://www.iucnredlist.org/assessment/updates) for more information.) However, the specific ranking is not tied to any individual, and there is no geographical information included.\n\n## Considerations for Using the Data\n### Social Impact of Dataset\n\nThe hope is that this dataset could be helpful in conservation efforts or biodiversity research.\n\n### Discussion of Biases\n\nInclusion of a species in this dataset required that EOL provided at least 30 images of it, so there are only 400 of the 25,000 species in these categories included, and only 30 images per species. Additionally, all included species are in the kingdom, _Animalia_, and within 5 phyla.\n\n\n## Additional Information\n\n### Dataset Curators\n\nSamuel Stevens, Jiaman Wu, Matthew J. Thompson, and Elizabeth G. Campolongo\n\n### Licensing Information\n\nThe data (images and text) contain a variety of licensing restrictions ranging from [CC0](https://creativecommons.org/publicdomain/zero/1.0/) to [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/). Each image and text in this dataset is provided under the least restrictive terms allowed by its licensing requirements as provided to us (i.e, we impose no additional restrictions past those specified by licenses in the license file).\n\nThis dataset (the compilation) has been marked as dedicated to the public domain by applying the [CC0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). However, images may be licensed under different terms (as noted above).\nFor license and citation information by image, see our [license file](https://huggingface.co/datasets/imageomics/rare-species/blob/main/metadata/licenses.csv).\n\n\n### Citation Information\n\n```\n@dataset{rare_species_2023,\n author = {Samuel Stevens and Jiaman Wu and Matthew J Thompson and Elizabeth G Campolongo and Chan Hee Song and David Edward Carlyn and Li Dong and Wasila M Dahdul and Charles Stewart and Tanya Berger-Wolf and Wei-Lun Chao and Yu Su},\n title = {Rare Species},\n year = {2023},\n url = {https://huggingface.co/datasets/imageomics/rare-species},\n doi = {},\n publisher = {Hugging Face}\n}\n```\nPlease also cite our paper: \n```\n@article{stevens2023bioclip,\n title = {BIOCLIP: A Vision Foundation Model for the Tree of Life}, \n author = {Samuel Stevens and Jiaman Wu and Matthew J Thompson and Elizabeth G Campolongo and Chan Hee Song and David Edward Carlyn and Li Dong and Wasila M Dahdul and Charles Stewart and Tanya Berger-Wolf and Wei-Lun Chao and Yu Su},\n year = {2023},\n eprint = {2311.18803},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}}\n```\n\n\nPlease be sure to also cite the original data sources and all constituent parts as appropriate. \n\n\n**EOL and IUCN classification data:**\n\nIUCN. 2022. The IUCN Red List of Threatened Species. Version 2022-2. https://www.iucnredlist.org. Accessed on 5 July 2023. https://www.iucnredlist.org/search?permalink=ab8daad6-d564-4370-b8e6-9c5ac9f8336f.\n\nEncyclopedia of Life. Available from http://eol.org. Accessed 29 July 2023.\n\n\nFor license and citation information by image, see our [license file](https://huggingface.co/datasets/imageomics/rare-species/blob/main/metadata/licenses.csv).\n\n\n### Contributions\n\nThe [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.\n"
},
{
"path": "docs/imageomics/ToL-10M_DatasetCard.md",
"content": "---\nLicense: cc0-1.0\nlanguage:\n- en\n- la\npretty_name: TreeOfLife-10M\ntask_categories:\n- image-classification\n- zero-shot-classification\ntags:\n- biology\n- images\n- animals\n- evolutionary biology\n- CV\n- multimodal\n- clip\n- biology\n- species\n- taxonomy\n- knowledge-guided\n- imbalanced\nsize_categories: 10M\n- **Repository:** [Imageomics/bioclip](https://github.com/Imageomics/bioclip)\n- **Paper:** BioCLIP: A Vision Foundation Model for the Tree of Life ([arXiv](https://doi.org/10.48550/arXiv.2311.18803))\n\n\n### Dataset Summary\n\nWith over 10 million images covering 454 thousand taxa in the tree of life, TreeOfLife-10M is the largest-to-date ML-ready dataset of images of biological organisms paired with their associated taxonomic labels. It expands on the foundation established by existing high-quality datasets, such as iNat21 and BIOSCAN-1M, by further incorporating newly curated images from the Encyclopedia of Life (eol.org), which supplies most of TreeOfLife-10M’s data diversity. Every image in TreeOfLife-10M is labeled to the most specific taxonomic level possible, as well as higher taxonomic ranks in the tree of life (see [Text Types](#text-types) for examples of taxonomic ranks and labels). TreeOfLife-10M was generated for the purpose of training [BioCLIP](https://huggingface.co/imageomics/bioclip) and future biology foundation models. \n\n\n\n||\n|:--|\n|**Figure 1.** Treemap from phyla down to family for TreeOfLife-10M. Interactive version available in [`visuals`](https://huggingface.co/datasets/imageomics/TreeOfLife-10M/tree/main/visuals) folder.|\n\n\n### Supported Tasks and Leaderboards\n\nImage Classification, Zero-shot and few-shot Classification.\n\n\n### Languages\nEnglish, Latin\n\n## Dataset Contents\n\n```\n/dataset/\n EOL/\n image_set_01.tar.gz\n image_set_02.tar.gz\n ...\n image_set_63.tar.gz\n metadata/\n catalog.csv\n species_level_taxonomy_chains.csv\n taxon.tab\n licenses.csv\n visuals/\n kingodm_ToL_tree.html\n kingdom_ToL_tree.pdf\n phyla_ToL_tree.html\n phyla_ToL_tree.pdf\n phyla_ToL_tree.png\n```\n\nEach `image_set` is approximately 30GB and contains 100 thousand images, each named `.jpg`. \nWe cannot reproduce the `iNat21` data, but after downloading it and BIOSCAN-1M, one can follow the directions from step 6 of [docs/imageomics/treeoflife10m.md](https://github.com/Imageomics/bioclip/blob/main/docs/imageomics/treeoflife10m.md) in the BioCLIP GitHub repo to combine them with the EOL data into the proper webdataset structure. This will produce a collection of files named `shard-######.tar` in a `train`, `val`, and `train_small` folder with which to work.\n\nInside each shard is a collection of images (named `.jpg`), for which each has the following files:\n```\n.com.txt\n.common_name.txt\n.jpg\n.sci.txt\n.sci_com.txt\n.scientific_name.txt\n.taxon.txt\n.taxonTag.txt\n.taxonTag_com.txt\n.taxon_com.txt\n.taxonomic_name.txt\n\n```\n\n\n### Data Instances\n\n\nThis dataset is a collection of images with associated text. The text matched to images contains both [Linnaean taxonomy](https://www.britannica.com/science/taxonomy/The-objectives-of-biological-classification) (kingdom through species) for the particular subject of the image and its common (or vernacular) name where available. There are 8,455,243 images with full taxonomic labels.\n\n\n### Data Fields\n\n#### Metadata Files\n\n`catalog.csv`: contains the following metadata associated with each image in the dataset\n - `split`: indicates which data split the image belongs to (`train`, `val`, or `train_small`), `train_small` is a duplicated subset of `train` and thus should not be included when analyzing overall stats of the dataset.\n - `treeoflife_id`: unique identifier for the image in the dataset.\n - `eol_content_id`: unique identifier within EOL database for images sourced from [EOL](https://eol.org). Note that EOL content IDs are not stable.\n - `eol_page_id`: identifier of page from which images from EOL are sourced. Note that an image's association to a particular page ID may change with updates to the EOL (or image provider's) hierarchy. However, EOL taxon page IDs are stable.\n - `bioscan_part`: indicates to which of the 113 data chunks of [BIOSCAN-1M](https://github.com/zahrag/BIOSCAN-1M#-iv-rgb-images) each image belongs. Note that there are 10K images per chunk and 8,313 in chunk #113.\n - `bioscan_filename`: unique identifier within BIOSCAN-1M dataset for images sourced from [BIOSCAN-1M](https://github.com/zahrag/BIOSCAN-1M).\n - `inat21_filename`: unique identifier within iNat21 dataset for images sourced from [iNat21](https://github.com/visipedia/inat_comp/blob/master/2021/README.md). \n\n - `inat21_cls_name`: `______` as labeled by iNaturalist.\n - `inat21_cls_num`: Number assigned by iNat21 to the given species (unique identifier for that species within iNat21 dataset).\n The remaining terms describe the _Linnaean taxonomy_ of the subject of the image; they are sourced as described in [Annotation Process, below](#annotation-process).\n - `kingdom`: kingdom to which the subject of the image belongs (`Animalia`, `Plantae`, `Fungi`, `Chromista`, `Protozoa`, `Bacteria`, `Viridiplantae`, `Protista`, `Orthornavirae`, `Bamfordvirae`, `Archaea`, or `Shotokuvirae`). Note: this large number of kingdoms are considered in recognition of the fact that there is not agreement on merging them.\n - `phylum`: phylum to which the subject of the image belongs.\n - `class`: class to which the subject of the image belongs.\n - `order`: order to which the subject of the image belongs.\n - `family`: family to which the subject of the image belongs.\n - `genus`: genus to which the subject of the image belongs.\n - `species`: species to which the subject of the image belongs.\n - `common`: common name associated with the subject of the image where available. Otherwise, this is the scientific name (`genus-species`), else whatever subset of the taxonomic hierarchy is available (eg., `kingdom-phylum-class-order` or `kingdom-phylum-class-order-family`). All images have a non-null entry for this column.\n\nNote that the `species` column occasionally has entries such as \"sp. ___(get ex)\" with some string following. This seems to be used to indicate the species is unknown, but various specimens/images are known to be the same species. Additionally, for `species` values containing an `x` between names, this is indicative of a hybrid that is a cross of the two species listed on either side of the `x`.\n\n##### Text Types\n\n| Text Type | Example |\n| ---- | -------- |\n| Common | black-billed magpie |\n| Scientific | _Pica hudsonia_ |\n| Taxonomic | _Animalia Chordata Aves Passeriformes Corvidae Pica hudsonia_ |\n\n\n`species_level_taxonomy_chains.csv`: CSV with the ITIS taxonomic hierarchy, indicated as follows:\n - `hierarchy_string_tsn`: string of Taxonomic Serial Numbers (TSN)* for the names of the ranks provided from highest to lowest, connected by dashes (eg., `202422-846491-660046-846497-846508-846553-954935-5549-5550`).\n - `hierarchy_string_names`: string of the names of the ranks provided from highest to lowest, connected by arrows (eg., `Plantae->Biliphyta->Rhodophyta->Cyanidiophytina->Cyanidiophyceae->Cyanidiales->Cyanidiaceae->Cyanidium->Cyanidium caldarium`).\n - `terminal_tsn`: Taxonomic Serial Number (TSN)* of designated species (eg., `5550`).\n - `terminal_scientific_name`: scientific name (``) of subject.\n - `terminal_vernacular`: vernacular or common name(s) of the subject, multiple names are separated by commas (eg., `rockskipper`, `Highland Small Rice Rat, Páramo Colilargo`).\n - `terminal_vernacular_lang`: language(s) of the vernacular name(s) provided; when there are multiple names, language is listed for each, separated by commas (eg., `English`, `English, English`, respectively for the vernacular name examples above).\n - `hierarchy_string_ranks`: string of ranks provided from highest to lowest, connected by arrows (eg., `Kingdom->Subkingdom->Phylum->Subphylum->Class->Order->Family->Genus->Species`).\n The remaining columns consist of the hierarchy string ranks describing the Linnaean taxonomy of the subject (as defined above), with `` filled in the `Species` column.\n\n*ITIS assigns a Taxonomic Serial Number (TSN) to each taxonomic rank; this is a stable and unique ID.\n\n\n`taxon.tab`: Tab-delimited file with taxonomic information for EOL images based on EOL page IDs. \n - `taxonID`: unique identifier for the file.\n - `source`: often `:` where the source corresponds to the domain of the `furtherInformationURL`. The ID likely corresponds to an ID at the source.\n - `furtherInformationURL`: URL with more information on the indicated taxon.\n - `acceptedNameUsageID`: `taxonID` for the name accepted to represent this entry. Less than a third of these are non-null\n - `parentNameUsageID`: `taxonID` of taxonomic rank above the indicated `taxonRank` in the hierarchy (eg., the `taxonID` of the genus `Atadinus` for the `Atadinus fallax (Boiss.) Hauenschild` entry).\n - `scientificName`: scientific name associated with the EOL page (``, authority as available).\n - `taxonRank`: lowest rank of the taxonomic tree indicated (eg., `genus` or `species`), occasionally not indicated, even for accepted names.\n - `taxonomicStatus`: whether the name is accepted by EOL or not (`accepted` or `not accepted`, correspond to existence of non-null `eolID` or `acceptedNameUsageID` entry, respectively).\n - `datasetID`: generally corresponds to the source identified in `source` column.\n - `canonicalName`: the name(s) associate with the `taxonRank` (eg., `` for species).\n - `authority`: usually name of person who assigned the name, with the year as available.\n - `eolID`: the EOL page ID (only non-null when `taxonomicStatus` is accepted by EOL).\n - `Landmark`: numeric values, meaning unknown, mostly null.\n - `higherClassification`: labeling in the EOL Dynamic Hierarchy above the `taxonRank` (eg., `Life|Cellular Organisms|Eukaryota|Opisthokonta|Metazoa|Bilateria|Protostomia|Ecdysozoa|Arthropoda|Pancrustacea|Hexapoda|Insecta|Pterygota|Neoptera|Endopterygota|Coleoptera|Adephaga|Carabidae|Paussus`).\n\n\n\n`licenses.csv`: File with license, source, and copyright holder associated to each image from EOL listed in `catalog.csv`; `treeoflife_id` is the shared unique identifier to link the two files. Columns are\n - `treeoflife_id`, `eol_content_id`, and `eol_page_id` are as defined above.\n - `md5`: MD5 hash of the image.\n - `medium_source_url`: URL pointing to source of image.\n - `eol_full_size_copy_url`: URL to access the full-sized image; this is the URL from which the image was downloaded for this dataset (see [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) for more information on this process).\n - `license_name`: name of license attached to the image (eg., `cc-by`).\n - `copyright_owner`: copyright holder for the image, filled with `not provided` if no copyright owner was provided. \n - `license_link`: URL to the listed license, left null in the case that `License Name` is `No known copyright restrictions`.\n - `title`: title provided for the image, filled with `not provided` if no title was provided.\n\n\n### Data Splits\n\nAs noted above, the `split` column of `catalog.csv` indicates to which split each image belongs. Note that `train_small` is a 1M-image, uniformly sampled, subset of `train` used for fine-tuned ablation training and all entries with this label are also listed with the `train` label. The `val` label is applied to images used for validation. \n\n10 biologically-relevant datasets were used for various tests of [BioCLIP](https://huggingface.co/imageomics/bioclip) (which was trained on this dataset), they are described (briefly) and linked to below.\n\n#### Test Sets\n\n- [Meta-Album](https://paperswithcode.com/dataset/meta-album): Specifically, we used the Plankton, Insects, Insects 2, PlantNet, Fungi, PlantVillage, Medicinal Leaf, and PlantDoc datasets from Set-0 through Set-2 (Set-3 was still not released as of our publication/evaluation (Nov. 2023).\n - [Birds 525](https://www.kaggle.com/datasets/gpiosenka/100-bird-species): We evaluated on the 2,625 test images provided with the dataset.\n - [Rare Species](https://huggingface.co/datasets/imageomics/rare-species): A new dataset we curated for the purpose of testing this model and to contribute to the ML for Conservation community. It consists of 400 species labeled Near Threatened through Extinct in the Wild by the [IUCN Red List](https://www.iucnredlist.org/), with 30 images per species. For more information, see our dataset, [Rare Species](https://huggingface.co/datasets/imageomics/rare-species).\n\nFor more information about the contents of these datasets, see Table 2 and associated sections of [our paper](https://doi.org/10.48550/arXiv.2311.18803).\n\n## Dataset Creation\n\n### Curation Rationale\n\nPreviously, the largest ML-ready biology image dataset was [iNat21](https://github.com/visipedia/inat_comp/tree/master/2021), which consists of 2.7M images of 10K species. This is significant breadth when comparing to popular general-domain datasets, such as [ImageNet-1K](https://huggingface.co/datasets/imagenet-1k); 10K species are rather limited when considering the vast scope of biology. For context, in 2022, [The International Union for Conservation of Nature (IUCN)](https://www.iucnredlist.org/) reported over 2M total described species, with over 10K distinct species of birds and reptiles alone. Thus, the lesser species diversity of iNat21 limits its potential for pre-training a foundation model for the entire tree of life. \n\nWith this focus on species diversity and the need for high-quality images of biological organisms, we looked to the [Encyclopedia of Life Project (EOL)](https://eol.org/). EOL is an image aggregator that collaborates with a variety of institutions to source and label millions of images. After downloading 6.6M images from EOL, we were able to expand our dataset to cover an additional 440K taxa. \n\nInsects (of the class Insecta with 1M+ species), birds (of the class Aves with 10K+ species) and reptiles (of the class Reptilia with 10K+ species) are examples of highly diverse subtrees with many more species than other taxonomic classes. This imbalance among subtrees in the tree of life present challenges in training a foundation model that can recognize extremely fine-grained visual representations of these classes. To help address this challenge for insects, we incorporated [BIOSCAN-1M](https://zenodo.org/doi/10.5281/zenodo.8030064), a recent dataset of 1M expert-labeled lab images of insects, covering 494 different families. The added variety of lab images, rather than in situ images (as in iNat21), further diversifies the _image_ distribution of TreeOfLife-10M.\n\nOverall, this dataset contains approximately 454K unique taxonomic labels of the more than 2M recorded by [IUCN](iucnredlist.org) in 2022. To the best of our knowledge, this is still the most diverse and largest such ML-ready dataset available, hence our curation. \n\n\n### Source Data\n\n[iNat21 data](https://github.com/visipedia/inat_comp/tree/master/2021#data) was downloaded, unzipped, and our compilation scripts pointed to the training split. As per their [terms of use](https://github.com/visipedia/inat_comp/tree/master/2021#terms-of-use), the data is catalogued, but not reproduced, here.\n\n[BIOSCAN-1M](https://zenodo.org/doi/10.5281/zenodo.8030064): Collection of insect images hand-labeled by experts.\n\n[EOL](https://eol.org/): Biological image aggregator.\n\n#### Initial Data Collection and Normalization\n\n[iNat21 training data](https://github.com/visipedia/inat_comp/tree/master/2021#data) and [BIOSCAN-1M data](https://zenodo.org/doi/10.5281/zenodo.8030064) were downloaded and assigned `treeoflife_id`s for unique identification within the TreeOfLife-10M dataset. The iNat21 training data is formatted into a webdataset format prior to `treeoflife_id` assignments, since this is also used for a comparison to [BioCLIP](https://huggingface.co/imageomics/bioclip) as trained on the full TreeOfLife-10M dataset. For more detailed information on this process, please see [How to Create TreeOfLife-10M](https://github.com/Imageomics/bioclip/tree/main/docs/imageomics/treeoflife10m.md#how-to-create-treeoflife-10m) in the BioCLIP GitHub repo. \n\nFirst, media manifest data was sourced from EOL using [this script](https://github.com/Imageomics/bioclip/blob/main/scripts/get_media_manifest.py). The media manifest includes EOL content and page IDs from which to connect the taxonomic information, along with source URLs and licensing information. The `EOL Full-Size Copy URL` was then used to download all the images, naming each `__eol_full-size-copy.jpg` for reference back to the media manifest. [Scripts](https://github.com/Imageomics/bioclip/tree/main/scripts/evobio10m) to perform these downloads and [instructions](https://github.com/Imageomics/bioclip/blob/main/docs/imageomics/treeoflife10m.md) can be found in the [BioCLIP GitHub repository](https://github.com/Imageomics/bioclip).\n\nSee [below](#Annotation-Process) for details of annotation following data collection.\n\nSpecies selected for the Rare Species dataset were removed from this dataset (see [Initial Data Collection and Normalization of Rare Species](https://huggingface.co/datasets/imageomics/rare-species#initial-data-collection-and-normalization)).\n\n\n### Annotations\n\n#### Annotation Process\n\nAnnotations were primarily sourced from image source providers. \n\nFor iNat21 and BIOSCAN-1M images, the labels provided by those sources were used.\n- iNat21: iNaturalist English vernacular names and taxa were used.\n- BIOSCAN-1M: Linnaean taxonomic rankings were applied as labeled in the [BIOSCAN-1M dataset](https://zenodo.org/doi/10.5281/zenodo.8030064), which is all hand-labeled by experts. Note that the dataset provides other ranks (not considered in the 7-rank Linnaean taxonomy), such as tribe, which were not included in this dataset.\n\nFor images from EOL, the scientific name (`genus-species`) was used to look up the higher-order taxa from the following sources as listed: BIOSCAN-1M metadata, EOL aggregate datasets (described below), then match this against the ITIS hierarchy for the higher-order taxa standardization. A small number of these are [homonyms](https://en.wikipedia.org/wiki/Homonym_(biology)), for which a list was generated to ensure proper matching of higher-order taxa (manual homonym resolution is in class `NameUpgrader` in the [naming script](https://github.com/Imageomics/bioclip/blob/main/src/imageomics/naming.py)). After these resources were exhausted, any remaining unresolved taxa were fed through the [Global Names Resolver (GNR) API](https://resolver.globalnames.org/api). Despite our efforts, we discovered after training that some hemihomonyms were mislabeled at higher-level taxa (family up to kingdom). This impacts approximately 0.1-0.2% of our data. We are in the process of developing a more robust solution to taxonomic labeling which will also account for re-naming (as is currently in process for many bird species). We intend to release a patch alongside the solution.\n\nThis process allowed us to reach full taxa labels for 84% of images. To put this in perspective, 10% of images in TreeOfLife-10M are only labeled to the `family` level (no `genus-species` designations) as part of BIOSCAN-1M, so this places a cap on the taxa coverage. Taxonomic ranking also is not entirely standardized and agreed-upon throughout the biology community, so most gaps are more indicative of lack of consensus on label than missing information.\n\n\n#### Who are the annotators?\n\nSamuel Stevens, Jiaman Wu, Matthew J. Thompson, and Elizabeth G. Campolongo\n\n### Personal and Sensitive Information\nN/A\n\n## Considerations for Using the Data\n\n### Social Impact of Dataset\n\nThe hope is that this dataset could be helpful in conservation efforts or biodiversity research.\n\n### Discussion of Biases and Other Known Limitations\n\nThis dataset is imbalanced in its representation of various species with the greatest representation available for those in the phyla _Arthropoda_, _Tracheophyta_, and _Chordata_ (see our [interactive treemap from phylum to family](https://huggingface.co/imageomics/treeoflife-10m/raw/main/phyla_ToL_tree.html) for further details of this distribution). This class imbalance is both a result of availability of images and actual variance in class diversity. Additionally, as noted above, there are 2M+ estimated species according to [IUCN](iucnredlist.org), so overall taxonomic coverage is still limited (though it far surpasses the species diversity of other well-known animal datasets).\n\nNot all data is labeled to the species level, and some entries are more or less precise. For instance, the `species` column occasionally has entries such as \"sp. ___(get ex)\" with some string following. This seems to be used to indicate the species is unknown, but various specimens/images are known to be the same species. Additionally, for `species` values containing an `x` between names, this is indicative of a hybrid that is a cross of the two species listed on either side of the `x`. Due to the additional information provided about the higher order taxa, these labeling anomalies still present valuable information providing links between these classes.\n\n\nAs stated above, 84% of images have full taxa labels. However, due to the incomplete standardization and agreement on the taxonomic hierarchy throughout the biology community, most gaps are more indicative of lack of consensus on label than missing information. \n\nNote that BIOSCAN-1M’s label granularity may still be limited for insects, as 98.6% of BIOSCAN-1M’s images are labeled to the family level but only 22.5% and 7.5% of the images have genus or species indicated, respectively. Lack of label granularity is an inherent challenge.\n\n\n## Additional Information\n\n### Dataset Curators\n\nSamuel Stevens, Jiaman Wu, Matthew J. Thompson, and Elizabeth G. Campolongo\n\n### Licensing Information\n\nThe data (images and text) contain a variety of licensing restrictions mostly within the CC family. Each image and text in this dataset is provided under the least restrictive terms allowed by its licensing requirements as provided to us (i.e, we impose no additional restrictions past those specified by licenses in the license file).\n\nPlease see the [iNat21 terms of use](https://github.com/visipedia/inat_comp/tree/master/2021#terms-of-use) for full information on use of their images.\n\nAll BIOSCAN-1M images are licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).\n\nEOL images contain a variety of licenses ranging from [CC0](https://creativecommons.org/publicdomain/zero/1.0/) to [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/).\nFor license and citation information by image, see our [license file](https://huggingface.co/datasets/imageomics/treeoflife-10m/blob/main/metadata/licenses.csv).\n\n**Note**: Due to licensing restrictions discovered after training, approximately 30K of the images used to train BioCLIP (about 0.3%) cannot be republished here and links to original content are no longer available. Overall, 14 families that were included in training BioCLIP are not republished in this dataset, a loss of 0.38% of the taxa diversity.\n\nThis dataset (the compilation) has been marked as dedicated to the public domain by applying the [CC0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). However, images may be licensed under different terms (as noted above).\n\n\n### Citation Information\n\n```\n@dataset{treeoflife_10m,\n author = {Samuel Stevens and Jiaman Wu and Matthew J Thompson and Elizabeth G Campolongo and Chan Hee Song and David Edward Carlyn and Li Dong and Wasila M Dahdul and Charles Stewart and Tanya Berger-Wolf and Wei-Lun Chao and Yu Su},\n title = {TreeOfLife-10M},\n year = {2023},\n url = {https://huggingface.co/datasets/imageomics/TreeOfLife-10M},\n doi = {},\n publisher = {Hugging Face}\n}\n```\nPlease also cite our paper: \n```\n@article{stevens2023bioclip,\n title = {BIOCLIP: A Vision Foundation Model for the Tree of Life}, \n author = {Samuel Stevens and Jiaman Wu and Matthew J Thompson and Elizabeth G Campolongo and Chan Hee Song and David Edward Carlyn and Li Dong and Wasila M Dahdul and Charles Stewart and Tanya Berger-Wolf and Wei-Lun Chao and Yu Su},\n year = {2023},\n eprint = {2311.18803},\n archivePrefix = {arXiv},\n primaryClass = {cs.CV}}\n```\n\n\nPlease be sure to also cite the original data sources and all constituent parts as appropriate.\n\n\n- iNat21:\n```\n@misc{inat2021,\n author={Van Horn, Grant and Mac Aodha, Oisin},\n title={iNat Challenge 2021 - FGVC8},\n publisher={Kaggle},\n year={2021},\n url={https://kaggle.com/competitions/inaturalist-2021}\n}\n```\n\n- BIOSCAN-1M:\n```\n@inproceedings{gharaee2023step,\n title={A Step Towards Worldwide Biodiversity Assessment: The {BIOSCAN-1M} Insect Dataset},\n booktitle = {Advances in Neural Information Processing Systems ({NeurIPS}) Datasets \\& Benchmarks Track},\n author={Gharaee, Z. and Gong, Z. and Pellegrino, N. and Zarubiieva, I. and Haurum, J. B. and Lowe, S. C. and McKeown, J. T. A. and Ho, C. Y. and McLeod, J. and Wei, Y. C. and Agda, J. and Ratnasingham, S. and Steinke, D. and Chang, A. X. and Taylor, G. W. and Fieguth, P.},\n year={2023},\n}\n```\n- EOL: Encyclopedia of Life. Available from http://eol.org. Accessed 29 July 2023.\n\nFor license and citation information by image, see our [license file](https://huggingface.co/datasets/imageomics/treeoflife-10m/blob/main/metadata/licenses.csv).\n\n\n- ITIS: Retrieved July, 20 2023, from the Integrated Taxonomic Information System (ITIS) on-line database, www.itis.gov, CC0\nhttps://doi.org/10.5066/F7KH0KBK\n\n\n### Contributions\n\nThe [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.\n"
},
{
"path": "docs/imageomics/evaluation.md",
"content": "# Evaluation\n\nHow are we going to evaluate CLIP-like models for use in biology?\n\n1. Zero-shot classification accuracy on IID data\n2. Zero-shot clasification accuracy on co-mimics\n3. Zero-shot classification accuracy on co-mimics with textual descriptions of the differences\n4. Data efficiency (OPEN QUESTION)\n5. Generalization to lab/museum photos (QUESTIONS)\n6. Trait presence (OPEN QUESTION)\n\n# Zero-shot Accuracy on iNaturalist21\n\nWe want to use 1K classes so we can compare to ImageNet1K classes.\nWe also want to compare performance on seen and unseen classes.\nSo we might include 9K classes during pretraining, then evaluate accuracy on 1K seen classes, then evaluate accuracy on the remaining 1K unseen classes.\n\nWe also want to compare common names and taxonomic names for the text encoder: common names are likely easier.\n\n# Butterflies Classification\n\nCan we classify museum photos of co-mimics?\nThey're deliberately visually challenging (to predators).\nWe would want to do zero-shot classification, perhaps with textual descriptions of the differences between species.\nWe could also use butterflies for zero-shot fine-grained classification of very similar classes, and to test whether CLIP generalizes to museum photos.\n\nData on `/fs/ess/PAS2136/Butterfly/Jiggins_dataset/Jiggins_datav2` might useful for this.\n\n# Data Efficiency\n\nHow can we do few-shot evaluation of CLIP models?\nShould we fine-tune?\nShould we do some sort of prompting or in-context learning?\n\nProbably can just do Tip-Adapter: this takes advantage of the text encoder, but doesn't require any training.\nWe can also do CLIP-Adapter which inserts a bottleneck layer (linear projection to lower dimension, then a linear projection to original dimension) and a residual connection after the vision and text encoders, which are tuned on the few-shot examples.\nOther options include Wise-FT which is a linear combination of the tuned weights and the original weights.\nLinear probing is consistently worse than zero-shot CLIP or these other options; do not use it.\n\n# Generalization to Lab/Museum Photos\n\nCan we do zero-shot classification of the fish photos from the Kenya team?\nThese photos are OOD in the sense that they are on white backgrounds (same applies to the butterflies).\n\nIdeally we would have some naturalist/citizen science photos of the same species, then see if the classification generalizes across background, rather than generalize across background *and* unseen species.\n\n# Presence of Traits\n\nCan we identify traits in a picture?\nWhat about in a species?\nGiven a picture of some animals, can we definitely say \"these traits are present in this photo\"?\nCan we say \"this animal has these traits, even though they are not visible\"?\n\nWhat dataset can we use for this? \nCan we construct a small one (~200-500 examples)?\n"
},
{
"path": "docs/imageomics/figure-creation.md",
"content": "# Figure Creation\n\nThis document describes how to create the t-SNE plot visualizations of the iNat validation set from BioCLIP's and OpenAI's model embeddings.\n\n## Extract Data\n\nOpenAI embeddings\n```\npython -m src.evaluation.extract_features --pretrained openai --exp_type openai --feature_output features --val_root [path_to_iNat21_val]\n```\n\nBioCLIP embeddings\n\n```\npython -m src.evaluation.extract_features --model hf-hub:imageomics/bioclip --exp_type bioclip --feature_output features --val_root [path_to_iNat21_val]\n```\n\n\n## Create t-SNE Figures\n\nOpenAI figures\n```\npython -m src.evaluation.hierarchy_tree_image --exp_type openai --rerun --remove_outliers --features_output features --results results --val_root [path_to_iNat21_val]\n\n```\n\nBioCLIP figures\n```\npython -m src.evaluation.hierarchy_tree_image --exp_type bioclip --rerun --remove_outliers --features_output features --results results --val_root [path_to_iNat21_val]\n```\n\n## Create Final Figure\n\n```\npython -m src.evaluation.create_final_figure --openai_tsne_data openai_tsne_rerun_top_6_remove_outliers --bioclip_tsne_data bioclip_tsne_rerun_top_6_remove_outliers --results_output results --val_root [path_to_iNat21_val]\n```\n"
},
{
"path": "docs/imageomics/training-data-osc.md",
"content": "# Training Data on OSC\n\nWe want to create a webdataset made up of the 10M images from iNat21, EOL and BIOSCAN.\n\nHere are the steps:\n\n1. Get a list of all images.\n2. Choose a validation split.\n3. Write the splits in the webdataset format.\n\n## 1. Get a list of all images\n\nEdit the `--tag` from v2 to v3.\n\n```sh\nsbatch slurm/make-dataset-mapping.sh\n```\n\n## 2. Choose a validation split\n\nYou have to pass a different `mapping.sqlite` file if you used a different tag in the previous step.\n\n```sh\npython scripts/evobio10m/make_splits.py \\\n --db /fs/ess/PAS2136/open_clip/data/evobio10m-v2/mapping.sqlite \\\n --seed 17 \\\n --val-split 5\n```\n\n## 3. Write the splits in webdataset formats\n\nEdit the `--tag` from v2 to v3 if you did in step 1.\n\n```sh\nsbatch slurm/make-dataset-wds.sh\n```\n\n## 4. Verification\n\nYou must point the `--shardlist` argument to the train and the val splits.\n\n```sh\npython scripts/evobio10m/check_wds.py --shardlist '/fs/ess/PAS2136/open_clip/data/evobio10m-v2.1/224x224/val/shard-{000003..000031}.tar' --workers 8\n```\n\nThis will print out some bad files, like:\n\n```\n/fs/ess/PAS2136/open_clip/data/evobio10m-v2.1/224x224/val/shard-000003.tar\n/fs/ess/PAS2136/open_clip/data/evobio10m-v2.1/224x224/val/shard-000004.tar\n/fs/ess/PAS2136/open_clip/data/evobio10m-v2.1/224x224/val/shard-000013.tar\n```\n\nThen you should delete these files and re-run the jobs.\n\n## Configuration\n\nThere are various configuration options hidden all over the codebase.\nHere are some different ones to look out for:\n\n* `src/imageomics/eol.py`: `VernacularNameLookup` has a default argument for `data/eol/vernacularnames.csv`. If your `vernacularnames.csv` is not in `data/eol`, you will have to edit this.\n* `src/imageomics/eol.py`: `EolNameLookup` has many default arguments for different files.\n* `src/iamgeomics/evobio10m.py`: `eol_root_dir`, `inat21_root_dir` and `bioscan_root_dir` all point to specific image folders on OSC.\n* `src/iamgeomics/evobio10m.py`: `get_output_dir` returns the default location for evobio10m datasets.\n* `scripts/evobio10m/make_wds.py`: `seen_in_training_json` and `unseen_in_training_json` are the seen and unseen species used in the rare species benchmarks. There are version controlled so they should be in the correct location in your repo.\n\n"
},
{
"path": "docs/imageomics/training-data-sources.md",
"content": "# Training Data\n\nWhere can we get more training data?\n\n1. [iNat](https://www.inaturalist.org/)\n2. Field Guides\n3. Birds\n4. [LILA BC](https://lila.science/)\n5. [Reddit](https://old.reddit.com/r/whatisthisanimal)\n6. [Merlin](https://www.allaboutbirds.org/guide/Merlin/overview)\n\n## iNaturalist\n\niNaturalist has lots of image data that might be useful.\n\n* [DarwinCore Archive](https://www.inaturalist.org/taxa/inaturalist-taxonomy.dwca.zip) is the entire iNat tree and all common names. Found [here](https://www.inaturalist.org/pages/developers).\n* [Amazon Open Data Program](https://www.inaturalist.org/blog/49564-inaturalist-licensed-observation-images-in-the-amazon-open-data-sponsorship-program) provides a way for researchers to download lots of media from iNat for free for research purposes.\n\nThere are comments on iNat as well.\nI haven't found any that aren't just species suggestions.\nBut if we could find \"debates\" about an image's species, there might be textual data describing individual traits (supporting evidence for a particular classification).\n\n\n## Field Guides\n\nField guides will likely have great textual descriptions of traits.\nMany of them (for birds) should be available in PDF form.\nThere are two challenges:\n\n1. Extracting images and text from PDFs, depending on whether we need OCR or if there is text embedded in the document.\n2. Copyright issues\n\n**Butterfly field guides:**\n* [Common Butterflies of the Chicago Region](https://fieldguides.fieldmuseum.org/sites/default/files/rapid-color-guides-pdfs/butterflyguide_new.pdf)\n* [Field Studies Council Butterflies guide](https://www.field-studies-council.org/shop/publications/butterflies-guide/)\n* [The Complete Field Guide to Butterflies of Australia](https://www.researchgate.net/publication/283572367_The_Complete_Field_Guide_to_Butterflies_of_Australia)\n* [eBMS Field Guides for butterfly Identification](https://butterfly-monitoring.net/field-guides)\n* [Field Guide to the Butterflies of Sri Lanka](https://www.researchgate.net/publication/329880548_Field_Guide_to_the_Butterflies_of_Sri_Lanka)\n\n**Heliconius:**\n* [UWI's The Online Guide to the Animals of Trinidad and Tobago](https://sta.uwi.edu/fst/lifesciences/sites/default/files/lifesciences/documents/ogatt/Heliconius_melpomene%20-%20Postman%20Butterfly.pdf)\n\n## Bird datasets\n* [Avibase](https://avibase.bsc-eoc.org/avibase.jsp?lang=EN)\n* [Kaggle BIRDS 515 SPECIES- IMAGE CLASSIFICATION](https://www.kaggle.com/datasets/gpiosenka/100-bird-species)\n* [Birdsnap](https://paperswithcode.com/dataset/birdsnap)\n\n\n## LILA BC\n\nLILA has 10M labeled images.\nWe don't have textual descriptions, but 10M images is nothing to sneeze at.\n\n## Reddit\n\nReddit (and Twitter) have communities (`r/whatisthisanimal`, `r/animalid`) around identifying images of species.\nThere is likely lots of rich textual data describing animal traits. \nHowever, there is also likely a lot of noisy text data.\n\n\n## Merlin\n\nMerlin has lots of detailed pictures of animals at varying degrees of detail:\n\n* Adult male (Taiga)\n* Adult male (Prairie)\n* Female/immature (Taiga)\n* Etc\n\nThere are also detailed text descriptions:\n\n* \"Small stocky falcon with a blocky head. Males are generally dark overall, but their color varies geographically. The Taiga subspecies is medium gray above with a pale mustache stripe and a thin white eyebrow.\"\n\nThis sort of data is exactly what we want!\n\nHow do we get access to it?\n"
},
{
"path": "docs/imageomics/training.md",
"content": "# Training\n\n```sh\nsbatch --output=/fs/ess/PAS2136/open_clip/logs/slurm/%j-baseline.log --job-name=baseline slurm/train.sh\n```\n"
},
{
"path": "docs/imageomics/treeoflife10m.md",
"content": "# How to Create TreeOfLife-10M\n\n**Note:** \n- [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) has the EOL images, but not iNat21 or BIOSCAN-1M due to licensing restrictions. \n- To reconstruct the _full_ dataset, please follow the steps outlined below in _Reproduce TreeOfLife-10M_. This reproduction process is designed to be run on an HPC system using Slurm.\n\n## Reproduce TreeOfLife-10M\n\nAll of the following steps should be completed in the root directory of the repository. Start by setting up your conda environment with [`requirements-training.yml`](/requirements-training.yml):\n\n```\nconda env create -f requirements-training.yml --solver=libmamba -y\nconda activate bioclip-train\npip install -e .\n```\n\n1. **Download [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M)**:\n - _Optional:_ Change the dataset storage location and other Slurm parameters (within the \"customize\" section) in the component download setup script ([`scripts/setup_download_tol-10m_components.bash`](/scripts/setup_download_tol-10m_components.bash)).\n - Download [TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) components by running:\n ```bash\n\t sbatch --account scripts/submit_download_tol-10m_components.bash\n ```\n This will download the tar and metadata files from Hugging Face, as well as [iNat21](https://github.com/visipedia/inat_comp/tree/master/2021#data) and [BIOSCAN-1M](https://zenodo.org/doi/10.5281/zenodo.8030064) into `../data/TreeOfLife-10M/` relative to the script, in the format specified in [`disk_reproduce`](/src/imageomics/disk_reproduce.py).\n - Note: This launches a collection of scripts which can also be run individually.\n2. **[`make-dataset-wds_reproduce`](/slurm/make-dataset-wds_reproduce.sh)**:\n - This actually creates the webdataset files by running [`make_wds_reproduce`](/scripts/evobio10m/make_wds_reproduce.py) for each of the splits.\n - Make appropriate adjustments for your local setup to [`make-dataset-wds_reproduce`](/slurm/make-dataset-wds_reproduce.sh) (i.e., change account and path information, settings as described below).\n - On your HPC, run:\n ```bash\n\t sbatch --account slurm/make-dataset-wds_reproduce.sh\n ```\n - This runs the `scripts/evobio10m/make_wds_reproduce.py` for each of the splits using 32 workers.\n - It takes a long time (6 hours) and requires lots of memory.\n3. **[`check_wds`](/scripts/evobio10m/check_wds.py)**:\n - Checks for bad shards and records them.\n - Run\n ```bash\n\t sbatch --account --cpus-per-task slurm/check-wds.slurm \n ``` \n - Writes a list of bad shards to `logs/bad-shards.txt`.\n - For instance, if images are placed in the default location, run the following to check the training split:\n ```bash\n\t sbatch --account --cpus-per-task 32 slurm/check-wds.slurm 'data/TreeOfLife-10M/dataset/evobio10m-CVPR-2024/224x224/train/shard-{000000..000165}.tar'\n ``` \n4. **[`make_catalog_reproduce`](/scripts/evobio10m/make_catalog_reproduce.py)**:\n - Generates the catalog of all images in the dataset, which includes information about their original data source and taxonomic record.\n - Run\n ```bash\n\t sbatch --account --cpus-per-task slurm/make-catalog_reproduce.slurm \\\n\t --dir \\\n\t --db \\\n\t --tag \\\n\t --batch-size \n ```\n - Creates a file `catalog.csv` in `--dir` which is a list of all names in the webdataset.\n - **Note:** `mapping.sqlite` is a SQLite database comprised of just the `predicted-catalog.csv` and can be replaced by a SQLite database constructed from [TreeOfLife-10M/metadata/catalog.csv](https://huggingface.co/datasets/imageomics/TreeOfLife-10M/blob/main/metadata/catalog.csv), which may be overwritten on this step depending on where these are saved.\n - For instance, if images are placed in the default location, run the following to generate the catalog file:\n ```bash\n\t sbatch --account --cpus-per-task 32 slurm/make-catalog_reproduce.slurm \\\n\t --dir data/TreeOfLife-10M/dataset/evobio10m-CVPR-2024/224x224 \\\n\t --db data/TreeOfLife-10M/metadata/mapping.sqlite \\\n\t --tag CVPR-2024 \\\n\t --batch-size 256\n ```\n5. **[`check_taxa`](/scripts/evobio10m/check_taxa.py)**:\n - This will check the actual catalog file for any taxa issues.\n - More information on this file can be found [here](/scripts/README.md).\n - Run\n ```bash\n\t python scripts/evobio10m/check_taxa.py //data/evobio10m-CVPR-2024/catalog.csv\n ```\n\n\n## Original TreeOfLife-10M Generation\nThis was the process for creating the entire dataset, version 3.3 (which we used to train [BioCLIP](https://huggingface.co/imageomics/bioclip) for the public release).\n\n1. **[`download_data`](/scripts/download_data.sh)**:\n - Run `bash scripts/download_data.sh` to download most of the metadata files.\n2. **[`make_mapping`](/scripts/evobio10m/make_mapping.py)**:\n - Creates the sqlite database that maps from original files to tree of life ids.\n - Run `python scripts/evobio10m/make_mapping.py --tag v3.3 --workers 8`\n - Can run on login nodes and should take several hours. If you want it much faster, you can queue it on slurm with more workers.\n3. **[`make_splits`](/scripts/evobio10m/make_splits.py)**:\n - Adds the splits table to the sqlite database: marks each image as belonging to either val or train, and then picks out 10% of the training images to use as an ablation study.\n - Run `python scripts/evobio10m/make_splits.py --db /fs/ess/PAS2136/open_clip/data/evobio10m-v3.3/mapping.sqlite --val-split 5 --train-small-split 10 --seed 17`\n - This will run quickly on a login node.\n4. **[`make_metadata`](/scripts/evobio10m/make_metadata.py)**:\n - Creates all the metadata files that can be easily used by `make_wds.py`. \n - Also makes a `predicted-catalog.csv` file that will closely mimic `catalog.csv` (described below). `predicted-catalog.csv` includes rows for the rare species which are not included in `catalog.csv`.\n - See [ToL-EDA HF Repo](https://huggingface.co/datasets/imageomics/ToL-EDA) for more information about these files.\n - Run `python scripts/evobio10m/make_metadata.py --db /fs/ess/PAS2136/open_clip/data/evobio10m-v3.3/mapping.sqlite` \n5. **[`check_taxa`](/scripts/evobio10m/check_taxa.py)**:\n - This will check the predicted catalog file for any taxa issues. If there are major issues, fix them first.\n - Run `python scripts/evobio10m/check_taxa.py /fs/ess/PAS2136/open_clip/data/evobio10m-v3.3/predicted-catalog.csv` \n6. **[`make-dataset-wds`](/slurm/make-dataset-wds.sh)**:\n - This actually creates the webdataset files by running [`make_wds`](/scripts/evobio10m/make_wds.py) for each of the splits.\n - Run `sbatch slurm/make-dataset-wds.sh` on Pitzer.\n - This runs the `scripts/evobio10m/make_wds.py` for each of the splits using 32 workers.\n - It takes a long time (6 hours) and requires lots of memory.\n7. **[`check_wds`](/scripts/evobio10m/check_wds.py)**:\n - Checks for bad shards and records them.\n - Run `scripts/evobio10m/check_wds.py --shardlist SHARDS --workers 8 > logs/bad-shards.txt` \n - Writes a list of bad shards to `logs/bad-shards.txt`.\n8. **[`make_catalog`](/scripts/evobio10m/make_catalog.py)**:\n - Generates the catalog of all images in the dataset, which includes information about their original data source and taxonomic record.\n - Run `python scripts/evobio10m/make_catalog.py --dir /fs/ess/PAS2136/open_clip/data/evobio10m-v3.3/224x224/ --workers 8 --batch-size 256 --tag v3.3 --db /fs/ess/PAS2136/open_clip/data/evobio10m-v3.3/mapping.sqlite`\n - Creates a file `catalog.csv` in `--dir` which is a list of all names in the webdataset.\n - **Note:** `mapping.sqlite` is a SQLite database comprised of just the `predicted-catalog.csv` and can be replaced by a SQLite database constructed from [TreeOfLife-10M/metadata/catalog.csv](https://huggingface.co/datasets/imageomics/TreeOfLife-10M/blob/main/metadata/catalog.csv), which may be overwritten on this step depending on where these are saved.\n9. **[`check_taxa`](/scripts/evobio10m/check_taxa.py)**:\n - This will check the actual catalog file for any taxa issues.\n - More information on this file can be found [here](/scripts/README.md).\n - Run `python scripts/evobio10m/check_taxa.py /fs/ess/PAS2136/open_clip/data/evobio10m-v3.3/catalog.csv`\n\n\nThis process is buggy and doesn't always work.\n`make_wds.py` tries to re-write wds files that are corrupted, but it doesn't always work.\n`make_wds.py` also ignores images and species used in the rare species benchmark.\n"
},
{
"path": "docs/open_clip/Interacting_with_open_clip.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"YPHN7PJgKOzb\"\n },\n \"source\": [\n \"# Interacting with open_clip\\n\",\n \"\\n\",\n \"This is a self-contained notebook that shows how to download and run open_clip models, calculate the similarity between arbitrary image and text inputs, and perform zero-shot image classifications.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Preparation for colab\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = \\\"\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"0BpdJkdBssk9\",\n \"outputId\": \"4d9b51f8-d255-4868-97f6-be0a67dadfae\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: open_clip_torch in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (0.2.0)\\n\",\n \"Requirement already satisfied: matplotlib in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (3.5.1)\\n\",\n \"Requirement already satisfied: regex in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from open_clip_torch) (2022.3.15)\\n\",\n \"Requirement already satisfied: ftfy in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from open_clip_torch) (6.1.1)\\n\",\n \"Requirement already satisfied: torch>=1.9 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from open_clip_torch) (1.11.0)\\n\",\n \"Requirement already satisfied: webdataset>=0.2.5 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from open_clip_torch) (0.2.5)\\n\",\n \"Requirement already satisfied: torchvision in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from open_clip_torch) (0.12.0)\\n\",\n \"Requirement already satisfied: pyparsing>=2.2.1 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from matplotlib) (3.0.7)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.7 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from matplotlib) (2.8.2)\\n\",\n \"Requirement already satisfied: packaging>=20.0 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from matplotlib) (21.3)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from matplotlib) (1.4.2)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from matplotlib) (0.11.0)\\n\",\n \"Requirement already satisfied: numpy>=1.17 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from matplotlib) (1.22.3)\\n\",\n \"Requirement already satisfied: pillow>=6.2.0 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from matplotlib) (9.1.0)\\n\",\n \"Requirement already satisfied: fonttools>=4.22.0 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from matplotlib) (4.31.2)\\n\",\n \"Requirement already satisfied: six>=1.5 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\\n\",\n \"Requirement already satisfied: typing-extensions in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from torch>=1.9->open_clip_torch) (4.1.1)\\n\",\n \"Requirement already satisfied: pyyaml in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from webdataset>=0.2.5->open_clip_torch) (6.0)\\n\",\n \"Requirement already satisfied: braceexpand in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from webdataset>=0.2.5->open_clip_torch) (0.1.7)\\n\",\n \"Requirement already satisfied: wcwidth>=0.2.5 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from ftfy->open_clip_torch) (0.2.5)\\n\",\n \"Requirement already satisfied: requests in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from torchvision->open_clip_torch) (2.27.1)\\n\",\n \"Requirement already satisfied: certifi>=2017.4.17 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from requests->torchvision->open_clip_torch) (2021.10.8)\\n\",\n \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from requests->torchvision->open_clip_torch) (1.26.9)\\n\",\n \"Requirement already satisfied: idna<4,>=2.5 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from requests->torchvision->open_clip_torch) (3.3)\\n\",\n \"Requirement already satisfied: charset-normalizer~=2.0.0 in /home/rom1504/safety_patricks/laion-main/.env/lib/python3.8/site-packages (from requests->torchvision->open_clip_torch) (2.0.12)\\n\"\n ]\n }\n ],\n \"source\": [\n \"! pip install open_clip_torch matplotlib\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"C1hkDT38hSaP\",\n \"outputId\": \"70a44964-883d-4fd0-b95a-2c7f2b19aca9\"\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import torch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"eFxgLV5HAEEw\"\n },\n \"source\": [\n \"# Loading the model\\n\",\n \"\\n\",\n \"`clip.available_models()` will list the names of available CLIP models.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"uLFS29hnhlY4\",\n \"outputId\": \"11779e1e-8bdd-4167-c18e-d26bdd6b67db\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[('RN50', 'openai'),\\n\",\n \" ('RN50', 'yfcc15m'),\\n\",\n \" ('RN50', 'cc12m'),\\n\",\n \" ('RN50-quickgelu', 'openai'),\\n\",\n \" ('RN50-quickgelu', 'yfcc15m'),\\n\",\n \" ('RN50-quickgelu', 'cc12m'),\\n\",\n \" ('RN101', 'openai'),\\n\",\n \" ('RN101', 'yfcc15m'),\\n\",\n \" ('RN101-quickgelu', 'openai'),\\n\",\n \" ('RN101-quickgelu', 'yfcc15m'),\\n\",\n \" ('RN50x4', 'openai'),\\n\",\n \" ('RN50x16', 'openai'),\\n\",\n \" ('ViT-B-32', 'openai'),\\n\",\n \" ('ViT-B-32', 'laion400m_e31'),\\n\",\n \" ('ViT-B-32', 'laion400m_e32'),\\n\",\n \" ('ViT-B-32', 'laion400m_avg'),\\n\",\n \" ('ViT-B-32-quickgelu', 'openai'),\\n\",\n \" ('ViT-B-32-quickgelu', 'laion400m_e31'),\\n\",\n \" ('ViT-B-32-quickgelu', 'laion400m_e32'),\\n\",\n \" ('ViT-B-32-quickgelu', 'laion400m_avg'),\\n\",\n \" ('ViT-B-16', 'openai'),\\n\",\n \" ('ViT-L-14', 'openai')]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import open_clip\\n\",\n \"open_clip.list_pretrained()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32-quickgelu', pretrained='laion400m_e32')\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"IBRVTY9lbGm8\",\n \"outputId\": \"f06fd2fd-6126-475b-87d0-b10aa3b7da49\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model parameters: 151,277,313\\n\",\n \"Context length: 77\\n\",\n \"Vocab size: 49408\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.eval()\\n\",\n \"context_length = model.context_length\\n\",\n \"vocab_size = model.vocab_size\\n\",\n \"\\n\",\n \"print(\\\"Model parameters:\\\", f\\\"{np.sum([int(np.prod(p.shape)) for p in model.parameters()]):,}\\\")\\n\",\n \"print(\\\"Context length:\\\", context_length)\\n\",\n \"print(\\\"Vocab size:\\\", vocab_size)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"21slhZGCqANb\"\n },\n \"source\": [\n \"# Image Preprocessing\\n\",\n \"\\n\",\n \"We resize the input images and center-crop them to conform with the image resolution that the model expects. Before doing so, we will normalize the pixel intensity using the dataset mean and standard deviation.\\n\",\n \"\\n\",\n \"The second return value from `clip.load()` contains a torchvision `Transform` that performs this preprocessing.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"d6cpiIFHp9N6\",\n \"outputId\": \"880cb98e-1e5e-430e-8b59-4bf35fa554f9\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Compose(\\n\",\n \" Resize(size=224, interpolation=bicubic, max_size=None, antialias=None)\\n\",\n \" CenterCrop(size=(224, 224))\\n\",\n \" \\n\",\n \" ToTensor()\\n\",\n \" Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))\\n\",\n \")\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"preprocess\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"xwSB5jZki3Cj\"\n },\n \"source\": [\n \"# Text Preprocessing\\n\",\n \"\\n\",\n \"We use a case-insensitive tokenizer, which can be invoked using `tokenizer.tokenize()`. By default, the outputs are padded to become 77 tokens long, which is what the CLIP models expects.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from open_clip import tokenizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"qGom156-i2kL\",\n \"outputId\": \"050b0ce1-caba-47e1-f4ac-dba994599718\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"tensor([[49406, 3306, 1002, 256, 49407, 0, 0, 0, 0, 0,\\n\",\n \" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\\n\",\n \" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\\n\",\n \" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\\n\",\n \" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\\n\",\n \" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\\n\",\n \" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\\n\",\n \" 0, 0, 0, 0, 0, 0, 0]])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tokenizer.tokenize(\\\"Hello World!\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"4W8ARJVqBJXs\"\n },\n \"source\": [\n \"# Setting up input images and texts\\n\",\n \"\\n\",\n \"We are going to feed 8 example images and their textual descriptions to the model, and compare the similarity between the corresponding features.\\n\",\n \"\\n\",\n \"The tokenizer is case-insensitive, and we can freely give any suitable textual descriptions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"id\": \"tMc1AXzBlhzm\"\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import skimage\\n\",\n \"import IPython.display\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from PIL import Image\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"from collections import OrderedDict\\n\",\n \"import torch\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"%config InlineBackend.figure_format = 'retina'\\n\",\n \"\\n\",\n \"# images in skimage to use and their textual descriptions\\n\",\n \"descriptions = {\\n\",\n \" \\\"page\\\": \\\"a page of text about segmentation\\\",\\n\",\n \" \\\"chelsea\\\": \\\"a facial photo of a tabby cat\\\",\\n\",\n \" \\\"astronaut\\\": \\\"a portrait of an astronaut with the American flag\\\",\\n\",\n \" \\\"rocket\\\": \\\"a rocket standing on a launchpad\\\",\\n\",\n \" \\\"motorcycle_right\\\": \\\"a red motorcycle standing in a garage\\\",\\n\",\n \" \\\"camera\\\": \\\"a person looking at a camera on a tripod\\\",\\n\",\n \" \\\"horse\\\": \\\"a black-and-white silhouette of a horse\\\", \\n\",\n \" \\\"coffee\\\": \\\"a cup of coffee on a saucer\\\"\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 368\n },\n \"id\": \"NSSrLY185jSf\",\n \"outputId\": \"06451963-5ecb-4ddc-d0a8-24e9b110af7d\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 351,\n \"width\": 1087\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"original_images = []\\n\",\n \"images = []\\n\",\n \"texts = []\\n\",\n \"plt.figure(figsize=(16, 5))\\n\",\n \"\\n\",\n \"for filename in [filename for filename in os.listdir(skimage.data_dir) if filename.endswith(\\\".png\\\") or filename.endswith(\\\".jpg\\\")]:\\n\",\n \" name = os.path.splitext(filename)[0]\\n\",\n \" if name not in descriptions:\\n\",\n \" continue\\n\",\n \"\\n\",\n \" image = Image.open(os.path.join(skimage.data_dir, filename)).convert(\\\"RGB\\\")\\n\",\n \" \\n\",\n \" plt.subplot(2, 4, len(images) + 1)\\n\",\n \" plt.imshow(image)\\n\",\n \" plt.title(f\\\"{filename}\\\\n{descriptions[name]}\\\")\\n\",\n \" plt.xticks([])\\n\",\n \" plt.yticks([])\\n\",\n \"\\n\",\n \" original_images.append(image)\\n\",\n \" images.append(preprocess(image))\\n\",\n \" texts.append(descriptions[name])\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"WEVKsji6WOIX\"\n },\n \"source\": [\n \"## Building features\\n\",\n \"\\n\",\n \"We normalize the images, tokenize each text input, and run the forward pass of the model to get the image and text features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"id\": \"HBgCanxi8JKw\"\n },\n \"outputs\": [],\n \"source\": [\n \"image_input = torch.tensor(np.stack(images))\\n\",\n \"text_tokens = tokenizer.tokenize([\\\"This is \\\" + desc for desc in texts])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"id\": \"ZN9I0nIBZ_vW\"\n },\n \"outputs\": [],\n \"source\": [\n \"with torch.no_grad():\\n\",\n \" image_features = model.encode_image(image_input).float()\\n\",\n \" text_features = model.encode_text(text_tokens).float()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"cuxm2Gt4Wvzt\"\n },\n \"source\": [\n \"## Calculating cosine similarity\\n\",\n \"\\n\",\n \"We normalize the features and calculate the dot product of each pair.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"id\": \"yKAxkQR7bf3A\"\n },\n \"outputs\": [],\n \"source\": [\n \"image_features /= image_features.norm(dim=-1, keepdim=True)\\n\",\n \"text_features /= text_features.norm(dim=-1, keepdim=True)\\n\",\n \"similarity = text_features.cpu().numpy() @ image_features.cpu().numpy().T\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 831\n },\n \"id\": \"C5zvMxh8cU6m\",\n \"outputId\": \"22bca748-ab42-4888-c9da-8f22c21c6185\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Cosine similarity between text and image features')\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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vHMX6TLQ49NZ6RzKxoZi8zsz8ws8+Z2RNmNmFmDTM7aGa3mdm7zOxZJ7ldp5nZb2TPf9DMqmb2uJl92MxevkLPeZGZvc3MPmpm95rZsJk1s/8fNrOPmNkPm3U2cZGZ9ZnZT5jZv5jZI23rHzKzb5rZX5nZd5vNUquvs+d7jZlNmpnL/m4ys8FlWveybzdm9qW2tr6j7fYXmNnfmdmDZjZuZqNmdp+Z/ZmZLS3l06/veWb2N9lnMGlmR8zsTjN7p5mdu9T1LfG5P9f2Gn9rgWU/0basM7MfWWD529uWfdsS2nRZ9l7eZ2Yj2ef4cPYeXbnIdcz62WX37czvk/TitrvePuP1tf/tXOD5rsy2vVvNbJ+Z1c3sqJndbWbvXsn+ysz6zeynzOw/zWx3tk3GZjaW9VE3mtn/MbNXzNVXmNl17a93Bdu6xcx+PXufDplZzcyeMrOPm9lrO1hfaGZvMLN/NLNd2fZSzfb/z5jvPwcWua7dbe/BDYt8zPvbHvP+JbT7RVl/dEfWPzWy9+O2bL+fd9Kzts/pTW03v2me7fe69tco6e1tj3vxPI+b930wf1z8JTP7fPaeT5rvDx82s38ws9eareyZK/PHsZ82s/+apQ0fNrPvM5v7V0f7ti+1Tf7mPb7U92QR7Q3M7Foze7uZfcrMHs321aaZHTazu8zsL83s+hN5ng7aVTGzH8k+yyezPuyQ+b70bWZW7mCdLzDfl99t/rhWN7P9Znazmf2WLfJ4aWY3tL3/u9tuPydbz23ZemObo782s0vM7I/Mf+84nO1zDfPfd+4x3wf9iplduITXd6WtUr9/qhobT3X73XVJmrOKwXOvLqu/z/8MvvFr1WV77pu/6dfVVTFt2rD4EQ0f/Kg/GbFxMNR3vmTxlRewMmLX1Kj8yfO5Rsz0a1BRlkM2W0BlLsd0OFvv5lnvL1tF3erLll38eiWpoGLrsmOo8aqKk7pGJ/zJ78H+2Q9D/T3bFYX+sDc0+thJa9t8JqqHFSe+H6uU1q1ya5DL+41IBfVngY+Z1md9yqiGFLvF57MO60irv1g/R7+U94MN1TShxZ/Qx+o6VY9lWBvitKHRpv/sNhRnr2IwUNiiyPx3j6H6UyetbTg1ncrHsmH5aiyBQhW1LKELLMKpehzjN5nH9Ap4xjGzV0n6oKS5fklvyv6ukfQ2M/s3ST/inBtZ4XZ9t6S/kzRzKMTO7O+NZvZxSTc4t4T0rbmfryTpdkkXz7FIf/Z3jqTvk/Q7Zvb9zrmvL3L9JunnJP2GZn+v10m6Ovt7i6QnpCwdrENm9lOS3qOphKr/lPS9zrkTPqN8srYbM+uS9G5JPzbL3Rdlfz9pZj/lnPvbRayvIOn/yb/H7YG5iqRBSZdL+lkz+wXn3HuX0tYl+KKkl2aXX6Lpwcj2toaSXjTj5pdI+vs5ll8v6Yq2m25cqCHZc/yWpF/T8Yl352R/P2pmv+WcmzdB4mQxs02S3ivp9bPcvT77u1TSz5jZRyT9uHNuYhmf/9WS/kbSbJNR92R/OyV9m6RfkPTHkn55uZ5/KczsDZL+Wr7vaneapNdKeq2ZfUrS9yymX8gCen8n6ZJZ7t6R/b1c0m+a2S86595/As1fFlmQ9S8lzRZQ3pj9XSPpF83svZJ+wbkl/KI7Scwskt9Xf06+v5qpV35//UFJt5vZG51zD69AO35I0p9K2jBPG94ovw38qHPuluVuw1KY2TXyx765asttyP4uk/QTZvZVSd/nnNu3wu26TNI/SZoZcN8on5j1Ykk/bWYvd84tGH3J+sW/kfRds9y9Jft7rqRfM7P/J+lXlrqdm9nPS/oDqe1X4+zLRfLH7Z/S9ONsbl32d4l8P/ROM7vYOXf/POtc1X7/VPbAww3lg1cuPn/2jy4ITOefXdCtd9R1/64TG61SrabaszfWhz8+pv/z58OSpLf+SP+iR3DFsdOHPz4uSXrja3uWNCUDVkb7icju436WeWamLtejUR1b9AiWhqu1Rkf1zLHe/L4JjS55ZEx+8sxk6lbvkh6L5TVRPay8xnx3ZdOsy5gF6ioPanRib7b86nAuVaM5oWNjT+jRJ78gSSoX+7Vh3fGjELE68r6gW71zHlva+5RJjbZGZS68bt/fFVU+bo7rXHs/OKHRefsvrB2n6rEMa0N75YKewuz9iZmpOxrQSPPQkiod4JnpVDuWJS5RTZM6oD16Qg9Jkk7X2VRqOYlO1eMYv8k8kg7wTLRT0wPHo5IekTQiKZS0VT6IkB9JXivpLDN73nIEr2djvorBx7LndJLul3RI/qR5+wn610namJ2UP9G2FDQ94SCW9KikI5Jq8u/RBZLyIVc7JH3RzL7dOfflBV5PQdKHJH3PjLtGJT0s/173STpfavXAA52+kOw5f1fS/2676X2Sfso5t/j6tvPbqZXfbgJJH5X0yuz6kKSHJDXk36s8iFSQ9Ddm9pRz7r/nWlkWYP8n+e2m3SOSnpJ/zy+VVJL0HjNbqTpR7ckAzzGz7jmCI1fp+O3gJfOs9zpNJQ4ckrIJwub3Hkk/mV0el3SfpKqkMyWdkd1ukt5hZvudc3+9iHXOpiop/2yeralt51H593+ux0xjZudL+oymJ+Q05fuIo/L7Uf4ZSj74eb6ZXbdMyUnfJulfNf37wpD8fjwmHwzeLOksTX0Wq1JFycy+T9JHsqux/Gd7VD4Z6GJN7ZuvlPS3kr5/gfV9u6R/k9Q+gfeE/Htfk9/ft2a3D0r6ezM73Tn3Oyf8YjpkZs+TDzi3V3epyrd5RD5QeYn85xlK+hlJ55rZq2cJyObb76VSa4K7fZLumePph7L/vyy/TZwj6ezstmOSbp3jcXtneR098tvdy2bc9XDWhoL88Sn/FXq1pJvM7Hrn3N1zPM+SmdmvS/q9GTcfkrQra8NFmjqGXSTpRjN73Sz98pCm3s+KpidXfUXH7/vHvSdLkAfcc5Py79uwpFT+szlf/vOXpGsl3WJmVznnVirqcb78sWggu/6w/Gvskk98y/uvcyR91swuc85NzrUyM9sh6QvZ8rlEfp8fkk8yyiv4FCT9vKQLzey1zrn6YhqcJRz8Sdu6783WvUl+22v3l5LePOO2x+SPs035beRMTU/cmrOfXO1+/1S3/+BUV7Zty9w/dbdujiTVpy2/FMXtjyiZ8Q0ziqSffFO/fvdXF19g69M3TurQEb8iplZYG+qqtS6XNHcBlpIqko5NW37x650tl84rZs+52PVKUuxi7c5OiG7SaYo6K1CHZVJvjLcul4pzn2wsFXulCaneOPld9/2PfUL7Dn/ruNt7urbosnPfoDBgG1or8r6gOE+/0d6nLKXvqGdfQefr60ILFbmCYjWXtG6srlPxWIa1o55Ona4rB91zLlfK7mtfHpjNqXIs+4L71+NGp5tMp+ksnT3rWCSslFPxOMZvsikkHeCZ6g5JH5D0KefccQFAM9siH4z5Jfn95HL5wMPPr1B7/kE+IPYJSW9zzj3R1paz5Ueqf2d207WSfl9+9OeJGpIfvf8JSTc556YFnbPkgddI+iP5k98FSR82s3MWCKT/X01POLhX0q9K+u/2wJb5ybiukg/+zTaab0HZCMO/ltRehv+3nXOzjqY/QSu93fyU/AjU3ZLeJumTzrk0W7fJv6d/r6lEkP9nZue7uSek+llNTzi4RdJPOOfuamvzJvmRnD8q6c+0MvOLf1M+QN0rvw1dKx9Qmak9waAqH5zbZmYXOOceXGD5L83zPuReKf/+HpUfkf+R9m3efKnxD8sHlSTpj8zsQ52MHnXOHZT0Hdl6v6SpKRb+0Tn3jsWsw8x65QPIO7ObhuQrh3ywvU1ZdYz/Kel35UfkXiXpL+RHgJ+oP9XUd4WH5RM2vjjzvc7a8BL5fbm5DM+7VBvk941Efnv+E+fccFv7zpH0j5Kek930RjN771yVW8zsNPkgaf4Luyaf1PSXeTA02ye/Q/69zhNWftvM7nDOfXIZX9uimNl2Sf+hqYSDJyX9iqR/nbGdr5Pvj39J/rjznZLeIb9ttTjn8u33/ZqaYuFzzrkb5muHc+5N2ePeoamqJnfn61uk92kq4SCR3w7f1T4aPzt+vFp+NPg2+W3gY1nw/ITPepjZd2p6wsFT8vtZe79clq9K84fy/XKXpH8ys0udc60ak1kiRP5+7tT0KRbe5JzbfaLtneFh+f3hk5Luy9uby7aBH5P/fLolbZf0Vzo+QW25/KN8wsG/SvrV9uOnmfXLf74/mt10tvz3m5nJHvnyoXxyUXvCwd9I+o2s382Xu0jSn2uq780/z19cRHs3yX+mqaR3Svo/zrnW8B0z2yafSCMzu0LTEw4+IOk3nXNPztL2HZJeJV95aFZrpN8/pU1MTh2eKuW5R6J0Vfx94xPpnMvMZ8umUHEsjY6lqtb8c/7km/r1Kz+9bknVCvKpFS67qKgrLqFc51qQtn0VDlr5WccLs/uSRX51Tha93mhJ65WkB/Ut1VVVqEjn6NJFPw4rI02nflIHwdyn3PLAfpKuVN733KKwpGKhR2matKZU6Onaogt2vkJd5WWZmRDLJO8Lwnn6jfY+ZSl9R6rkuMfPJlSoWM0lrRur61Q8lmHtSNrGIwQ2z3Esuy9OV+MUEE4lp8qxrKiynFLFilvr3a6ztVPnKzBmqT+ZTsXjGL/JprC34Jno/c65q5xz754tcCxJzrkDzrlfl/RDbTe/xRY5b3cHNkr6Z0mvbU84yNryqHz54PYg1s9kJ9RPxKSk051zP+ec+9LMhIPsuZvOuY/JB+r2ZDdv0zwntc3sJfInwnP/LenZzrlPzRxJ65xLnXPfdM79vPwo0SUxs275IFuecJDIB9VXIuHgZGw3ecLBc51z/9EeKHLeRyX9RNvy50p64WwrMrONkn677aZbJH1be8JBtt5Dzrk3S3qXpLJ8qfxllX3uX2m7aa7qBe23v2eJyy84tYL8+zss6QXOuQ/M3Oadc1+Q9N1tN/Vr5YJwi/FHmhqt+5Skq51zfzEzoOqcm3TO/bGk/yEfJJOkH7ATnOs7C2JfkT+NpFc5526cLbkja8MnnXPfrxnB65OkW377/QHn3G+2Jxxk7XtEPujYPpL7RzW3P9bUKPpU0uucc3/aPvo62yc/LZ9E0z4y/a+yhK2T7X2amgLgHklXOOc+Mst2fsw59yuaqvghSb+cJVqsOjP7XvkpfSSfwPJq59wvuxnl/7Pjx7/Ll9DPg83nSXrrMrQhkh+9njsg6dpZ+uWac+498sl5+ZjnAfnEu9Vyo6TznXN/4Jy7Z2bCgdTaBv5YPhEib/drzOy8FWrTBkl/4Zx7/czjp3NuJDsGfb7t5vn2zTdLen7b9Xc65368PeEgW+/98okrX2i7+efNbDG//CryyVZvds797/aEg2zd+9oSL1/VdtfXnXM3zJZwkD1uj3Puz51zV0h6YI7nPmn9vpndPtffYtfxTLbnW2dq391nauyxs/T4bWfo539yQH/5gRFd/pI9+vJNiytwNXQs0Sc/5z/aH/6eZ27pRZyY3e5BHch+pl2kq1WxuUckArnzzvgOveiqX9J11/yqrrv613TJOa9XHNf0zfv/TruemLOQHgAAwNPWtfZKvci+S9+m1+iFeoV26Fw9pUf1DX1Ox1asMCSeDvhNNh1JB3jGcc6NL7xUa9l/knRTdrVbfu7ulXBM0ltnC+Zl7Ugk/bh8OXjJ77s/Oduyi5UFbOYsXzxj2UPyo+ly8wVi26c42CfpjW4R0wss5XORWkH1L2qqAkRV0ne7zsvhz+skbjdvmRk8meHDmh7gvHaO5W7QVEWERD54Mt/n8Kvy5aBXSntSwHFJBFmQNk+geFy+/P18y8+cemQxSQeS9MvOuYfmutM59zVJN7fdNNf7u6Ky19deveOGhUZDO+c+I+n9bTf9rxNsxultlw8553Yt5kFu+aY0WaoPO+f+ea47s8Dh37TdNOtna2ZbNb3yyl9lyQVzrfdJ+QonuW2S3rCoFi+TbLR1XkmgKekNzrmhuR8hZX1lvt8UND2haTX9atvldzrn/mu+hbP3/5fabjrR7V7y0+PsaLv+tvn2P+fc5+RH1bcen41qP+myYPRCVV/yZb8mn/Ao+aoXr12hZu2Wr7wznz9uu3xWVk1gNu372j2SfnOuFWYJNz8in2Qp+df4M3MtP8NnnHPvX8Ry7f3k1xa57ln7yTXS75/yurumqgzkFQhmM1n19/V0n9jPYTPTju0F/fHbN+hP3rFBQ8dS/eD/PKDJyYUrKPzzJ8bVaPhpGX7g9SQdrBVBWzHIVHN/pUmy+8JFFo8MF73efCTYwut9yj2mR7LZxc7VZdpspy/wCJwMQVBsXU7TuUdHJdnI0LBt+dUQRWVtGbxU11z8ZoVhSXsO3KRDQ3PlxmG53eq+oK+4/zzu74nsJ+vUSLu5+432PmWxfZI0NcJvvj6p/bmXsm6srlPpWIa1J2yrbpAeNwvjlLwiQsSUPM94T7djmZmpbF06zy7XubpcTTV0r26ZVgUEK+tUOo7xm+x4JB0AC2sPQD57hZ7jQ4sIEB2Q9LG2m757rmVXyILvQ3bCvD1A/H9njhBcDtmUEzdJykf0DUl6qXPuE8v9XCegk+3m4Sx4Nads1OpX2266eI5F2xNDvuicu2+B9dbly2uvlC+2Xb4yK+/d7rmaSpL4QpYYkCdXfFtWyr5d+3b2pHPu4UW0YVx+OpGFfLnt8lzv70r7Pk3N131HVoVhMT7Qdvn6E2xDe5LKprUyEn4e713EMu2f7TlmNttZ3u+SD8Ln/mSWZWb6N01P2lmp4O1cbmi7/Ek3+3Qks2nfXl66fM3pTJY8cUV2tSnp3Yt86Eel1kRrpy/DiP32z2+Pph975/KnUmvyv1B+6odTwcn4jvO+mRU3ZvE1TY3Yl2bpe7PPtf32dzs3/6/+LCmlPRnpNQu0I7fY42F7P3nlIh8zl5Pa7zvnrp7rb7HrWIu2bZk6KbDvwNybx/6D/r6tm5fvZPiP/2C/SiXTvgOJPn3jwnm9+dQKL/+2Lm3awEn5taJ9ztD55vBczPyx09fbPk/t3HnAjew5F1rvfveEHtS3JEln6SKdsWLFarBUpeJUElG9MTbncvl97cuvpnKxT5vW+ZzufYe/tcqteeZoqD7rX5yd7M77jsY8/UZ7n7LYPql93fP1dYlLFGez5y1l3Vhdp8qxDGtTOZgaoVtL5565sJ7dVwqe2SN68fQ+lm3XmQoUqK6ajujAkh6Lzp0qxzF+k82Osxt4RstGy3+7pMvlR6f2aeqEb6593uDtK9SUOUfRzvApTY2C22Zm213bvNGdykpJXyfpavny1P3yI/TbA72Vtsvrzawyy8j5F8+4/tETbdtMZnaN/PuwKbtpj6TvcM6dtOEYK7jdzDq//CzaP/OBWdpX1PTgx1K2rz9c5LJLdad8csh6+YS36+QDtblva7ucj77+ovxUHuvlA5F3tC3TnnTQntAwn29myRULmff9PUna96V5E1FmaJ8+Y5uZbZtZln4JHpA0oam+4JNm9hbn3G0drm8lNSUtpl3tn63J93Uza6Q9r+3yA85PcTMv55wzs//Q1Gju582z+EpYju3lajOzxY6SXyHtr+MO59zRxTzIOVc3swc1lbBwjaRFVeaYQ/vn96nFvCfOud1mdldbG56n6dPEnHRm1id/rLpC0hmSeuWnIWk/trcnE63Ud5wFj23OuUkzG9LUFCEDsyw2c7/6z0U+/yc09d1pg5mdu4hEta8scH+uvd95mZm9S9LvOueOLPLx7dZCv3/Ku+Ccoswk56T7Hmro/HOOzy1LU6eHHvUnnS46b/lGGJdKpsF1gfYdSPTYE/PPbfvAroZuvcN/JfnhN/QtWxtw4ro1FQCe0Oi06znnnCazAnTdWtznV7SSCq6ophoa16gGtWXW5cY1uuB6D7qndL++KUnaoXN11gnPuofl1F3ZIH+odZqoHsquT+dcqsna0Wz5jSe3gfPIEyCqtWUfN4A5vNBeMe/9eR80oTE553R8Lv5Uv+GXX/wxpSdbtqGaGq6uos08peH7wU7WjdV1KhzLsHZ1R1NjhMabQ+qJZo4Z8tvPRDwsSbPej2eWp/OxLLBQBVdUXTVVNXcSDpbXqXAc4zfZ3Eg6wDOSmZ0hX8r3tVrafjCwIg3yJYI7We48TQ+iLUlW0v5n5UtTL/Vsx4B0XEpYe7n7w865PZ22bQ5XyweYe7Lr98gnHJyUk+snYbtZbMpk+7ecrlnu36HpSRCL3b4ekg/eLntttCwo+yVNVWB4iaYnHbQnEdzY9v8Ptt0/V9LBYqdWWK7392S4rO3yd5nZ5R2uZ6P8NCdLlgVy3y3p17ObrpB0q5ntkvTf8oHEm1dgP+/EUefc/BEeb+YvhNk+3/aEobtmuX8ud7dd3jZHYtayy6qAXNJ204+Z2f9Y5MPbk8mK8glUI8vVtg60b/dnmNlnlvDYM9oud3z2PkvC29l201K3gSuyy+fMs9yKMrNBSb8v6YelJaXxD6xIg5bW9+aRmYX2zQPZ1E+LcfeM6+dImi/pYGSh6lNt/kXSb0s6K7v+NklvNbOvyH9fuVnSLc65xZydWPV+/+mgtyfQNZeXdNuddX3+K5N63St7jlvmlm/VNDLqC2u85IWV4+7v1PhEqsNHfYnGhaZt+ODH/EmM9esCvfpljA5bSyIrqM+t06iO6agOapOOL/Q0oqHWaKn1rTzoha3TJh3SUxrSQZ2h40fB1Fy1dVJ0rvUedvt0r26Rk9NpOkvnddxVYKVEYUl93ds0OrFXR0ce1ab1x5+AHBnfqzjxI6jW95113P2rpVofliSF4epO+YAp67K+IFZToxpSvwaPW2ZIfnbGfq2fVhZ9IQPaIJPJyWlIh7RFx5cDPpqtu6TyrCf8sTat9WMZ1rYoKKq/sEkjzUM62nhSWypnH7fMcPOg4qyY3frSSuWu4+niVD6WxS5WQz5ZnCljTp61fhzjN9n82FPwjGNmz5L0WXV2cv34dLnlsajRnLMs13E6qZlVJP2HOi+pPdt7sb7t8mKDAUtxyYzrP7OUhAMzu0zSHy1i0V92zk0LUpyk7WYxo/BnOj499PjtYrGjhWMzG9FU0Of4JzP7BfmRs/M56Jx70yy336ippINWCehsW3xudvU+59zBtuXVtvyfZMufKenMGetdjOV6f0+G9m/gF2p6Qs9S9J9gO94uPxK6/fM8L/v7X5JkZo/KJ5D8TTYtxmro5LOVFt5/ZlZBmM/MZdfp+MSsldCv6d/nrjrBda1m0kH7dr9Z0ss7XM+JbPcDM653ug2synAPMztLPti9o4OHr9R3nJU4tp3ovjmf0QXub8mSs14p6ZOS8rNxBfljVn6ca5rZzZL+SdIH50lAWCv9/invja/t1W131vXhj4/pN39+/XFTKPzJXwxLkq6+rDRrJYS5xLFTFM39teDP3jesZpb+9sLnzJ3vk6ZOH/pXX1b9e/9Hr4rF1fqqgbls0Q6N6pgOaI/OcheqZNOTU57Iiun0ap26bfEnLrfodB3SUzqqgxpzw+q1gWn378nWW1S5dXK23VF3UPfoG3Jy2qozdMEJz+qClbJl8FKNTuzVgSP36KzTrjtuCoUn9vsiQL3d22athLASUpcosHDO+ydrR3X4mJ+ha6C3k68RWAk91qce169xjegJ7dJlMwo/1V1VB/SkJN93LUVkBW1wW3VY+7RHu7TZbZ82+jRxsfZmM8ht1umzjkzF2rVWj2U4NWytnKeR5iHtq+7S2T3PUjmcniS7e8KPCeorbKTSARa0lo9lqUsV2NwJ40/qYblsJs11c58uxwpYq8cxfpMtbP4hGMDTjJl1S/q4poIKTUn/KD+P7qXyQfOyc87yP0m/dRKattBcx7mZJ+5PJEDw+5qecPAt+RF6z5OfMqBbUtj2Ppx5/CqO036Gde4Jdzp3i6T2ksUfN7OlzD28Xj6ItdBfe/LEWt5u5jLzDPpity9p4eDQpVr4/Zs5zUauPTngQjPbml1+gaa25dYyzrknpOyboX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NeFRG/A56bUqprEsfIOtPeB7ydkYFLq/PHSyPifSml99VT9hjb7QSuBI6ussrC/LEaeC7wDxHxvJTSH2oo+13AO6n8GyiVewTZ6N73RcRTU0o/qf9T1CYiOoB/Ips3fczOikk6Nj0H+Azk4XpD9gXOA86LiB8Df5FS2jNKOZPSTqeZ3wClgJBzGR5gNSgilgHHli0+l7IO9YLjGTp+9ZMdv6fUVB0/mimywIqvkrX9or2AJ+WPV+fHiU01lNdBtk/eBFTKcVs637wAuDIi/iqldFvDH2BsxakVvlY4Vnw5ryPAkyNi75RSQ3fJI+LpwJdgxF2vfcnOq8+NiFenlD6Trx9kx8TzGdlujsgfL4uIp6WUfl9jHZ4PfAio1Buwf/54CvD3EfHKlNKPaym3UP7jyfZZ04ec5tcnH2RkG4TsGH1c/vhrYGf+Xe0sK2PSruki4vXAh4Hy4cIH5o/nRcRngdek1Pp55Sd630XEU8n+/1I+weSq/PFU4JKI+Mt66/5wULyZPXfEfwEzEcGcNI/tPFhzVoGe1EUvPQDMq1Ju6bVdbK87W0HpxkcQzGV+Xe9Vc62/YzelgZh7HTpyhDpAW1uw4qDZ3H3tDtbdsatp27798q0A7H3oqPFiw/T3DQxlZnjqStpnOJJ9qu0a2Dr4fF7boorrRARz2hayfWDTsPWlcqXzyVzmV81yUTwv7WY7C6p0LI8sOztnzmRW1TTkxXPpLrYPbmuiz4tqHtuQGtGq19Tb2Jy/vpDEAHelW1jHvXSRTWm0gMXsxyEsj8pBf5ocrdp+im7lWrrTbvroZQYzmc9i9uYAVrJ/1WOlJk+rtqHl7M1MZtFDF9dwMUekR7CAJUQEe9IubuM69rCTuSwYzGb4cNSUoAOyG2DFgIPtwO3ANqCd7Ibraobmsz8PODgiTh+tc2o8Isti8J18mwm4kWzE3l5kNwFL/hxYHhFPHKOj7ADg1wx1DEHWiXMDsIXsxu+h+fIZwJuBIyPivJRSd411fjPwkULZ1+dlryC7oQ5wGdCVb++YfFkX1TuTbm/CNksWMvwGa3de/hagl6xz6wiGOm6PBv4YEaemlG4ZrR5jaOpnrkVEvIOsg6NoA3Ar2fd7FAzeETwKuCAi/jyl9POy92wBSstmA2cXXvstUN7mxjPZS6lzuWQ3cBuwFRgAVgKHk/0mAc4CLo2IE1NKG+vYzn8Ar86f7yT7DewhaxulSSUDeG9ErC11GjXBDIYHHPQBdwCbyNrDYrL2VxqVegDwm4h4fEqpamdrRLwTeH/Z4nuBu/Ny55F9rmLHUfndvIvI9u9q4JB82YNkbbeSsb7njwKvz593k+3j7XkdhuXDm6Rj03OBb+R/9uVlbyY7ThzN0LH9qcDngeeNUtxktdPx2sPQb/dUhs5xd1D9GNPo+ewC4BX583NHWe8xDO3rknOBd1VZv1jWn1JKWxuoW2kfHMtQ5+wDwHVV1t8yRnmTffy4juwzLAGKo+fLj9XF9auKiLPy984ku7a4CVgPLCLr5C2121OB70XE2SlVn0QuslHo/ws8oeyl28j28wyy41rprs9JwMUR8diU0rWj1bUReWfq4wuLvlR6klK6JiKuBk4g+5wvAv65gW08Dvi/vIw+sn2+layTv3QcC+BTeRv4IfDvwP/LX9tBdk3XRbZvSqHJC4EfR8SRYwUpRcQHyAJfitaSZSTqJbuuLbXHfYDv58GF/13jx3wkWcBB6XroduA+siDYkTlN6xARHwP+pmzxHuBmsvPOPLL9WGoz86h8vT8p13QR8XdkARKlbVxPdj7bj6FzE2THwE3AO2otu4rSbx5qPyfXa8L2XUQ8A/gfhk8NUWrz3WSfaR/gdOBXZL8NFXTn6Q4BOplVdb1OZgMPDlu/9nKrz4E+M99mreUC9KU+1pA1jRXsa5rYKbZt49Dl8cIV1WPyFy7vBHawfWNPU7Z77a83cs/12c210/689pF7N/5uCzs292bvO88Rf62gu3BbqXOUhEmzYjbbge6BCbklpoeI0vlk5ijnnuJ5qZ7zT3f+39fRzpft0U5HmkEfvcPKnsjzoprLNqRGtOo19W6yWPp2OriCi9jOFoKgnQ566WEz69nMevZPqzk8TqipTmq+Vm0/RTvZShvttNFOD91sZmwwcqwAAQAASURBVB2bWcd93Mnx6QxmxKSNB1YFrdqG2qODE9KZXMPF7GArl/MbgjbaUtBPPx3MYD8OYTXHjDq94kNds4IOAP5EdoP1xymlER0ykaW9fQPw1ny7x5N16r65iXUo+grZTevvA29MKd1dqMshZDfpnpwvOgv4AEMj+Mrr3k7W4Vbs1Psc8M6U0vrCekcB/8VQKu4nMzRaeSwrgH8h63T7Z+BfU0qDuRojYh/gwZTS2/K/X8zQaNj1KaUn1bCNhrZZ9p4HyEZf/RC4qny0dGRpiJ9HdoN5GdkN9q+TdZQ0pMmfeUwRUfreSu4j6+z4UanzKCJmAS8n239z8sc3I+LYlNJ9hbpfSzbqlchSJhenWHhRSmlNk6t/G9k++hFwQ3lnV0Qszuv9HmAu2U3/T5MF39TiqWTf62bgLcA3UkqDd9oiS4f/dYZG530oIr6WUmrWEKAtZFlVvg9cXNx2vv0ZwJ+RjV5dRXbT/usRsbpSUFGeIaDYYfsT4C0ppZurrPtkslHAw3KeppRelK/zXobSOV/bYBs9kewYsgf4e+Azxf0XhSkSJunYtIysTfWT/a4/Uuy8zuvzVeC0fNFfRcR/jpFhYqLb6bjl+6/0272QoX331ZTSe5u8uQsKz/eJiCMqtUGGBxHsIQtkOjUi5pWPYq6w/gUVXh9TqQ1HNu1AaYqFX6aUXtxAcZN+/EgpfQT4SJ6d4DeF5Y2eP75L1pH4aeB9xZH++XXO58g+J8CZZKP2v1ZeSMFnGQo46CcLOPp4sdM8stTszwD+k6yzcRnwnTwQp3nDKzMvZiio6uoKgQ1fJgs6gGyKhbqDDoBv5tv4Z+Bfyo4nZ5IFYawku4b7YB6Y8f/IAhPeTPYb7M3XD+CVZPumneya4x8Ynq1hmIh4NcMDDn4AvCeldHXZeieSBcmcnpf96Yj4U0pp1MCU3GfI2sn3gLcWr4sjYi40Now5Iv6W4QEHd5N10n83pdRVtu5RwF8CrxmlyIm+pjuWLNhyT17Pz6Q0lH8u38ffZCj44K0R8eniNXu9Sr/5vPz3Mv5zcjVN33cRsTfZb6zU47wTeBvwhWKQYH6s/CRwGPDuJn2eh4wBhr6KNqr/J789f62fvqrrFPXXXG5HXeUC3MxVdLOHdjpYPSKhkSZbz56hy9IZndWzBsyYnbWD7t11JQ+raOv6br71nizw5Jhzl3HUWUvHeMeQy763DoB9Dp/LfkeaJaMV9KfajhdtUf/xQg8/pfbRPlpbKrxWT3sayOcaHq2dlrbdR++wsifyvKjmsg2pEa16Td1HFmi5iexWzEEcyYEcRkfMoCd1cRvXs5Y13MvtLEiL2TsORJOvVdsPwHL2YTHLWcSywQwtXWk393I7d3MrW9nEdfyRE4eNG9Vka+U2tCAWc1I6m+u4jB08SBo8G8IAA/TRSx+9g1M/PBw1K/fel1JKJ6aU/q1SwAFASmldSukdwAsLi18ZEYuaVIdyy4FvAeeV37xMKd1Blob8R4XFb8hv0FbyMuCMwt//nFJ6RbFTLy/3RrLOg18XFr85Imq5ezSbLBjjZSmlvy92/udlPzABWSHq3eZtwKqU0rtSSpdVSs+eUtqdUvocWWdLKf/IiXma4ZaXp7n+VGHROuCslNIPih2jKaWulNJ/kHVwl44ri4CPTVJVK7kAODyl9MGU0nWVRtemlB5MKX2YrDO1VO8/i4jDatzGMrKOnzNTSl8u7/RPKf0aeFZh0UKa11G8G9g/pfSmlNKF5dvOt9+bUvoOWQf4PfnifcjSklfyBIZGIt5Fdryo1NlLSmlD/pnPBn42ng8yhvlkgUBPTyl9rLxTsewYOxnHprnALOD5+W9/a4X6PBkoZiEYbb73yWin00pKaQPZiP+SatkOSsu7yDq3ITuGj7gSzgNSziosaijooMmm8vjRLMvIpid5dSqbWiCltI6svsUU6lV/C3lq9Ofmf/YCz0gpvS2VjdJPKQ2klL5HNnq+9Ns+DHjteD5IhfoE8JLCoi9VWO1reV0BDs0zP9RrKVka/bdXOJ78AXh+YdHReT32AOemlL5YCjjI108ppU+TBZqV/GXe4TtCRBzI8PP0P6WUnlkecJCXfRVwDnBhvmgWtQdZzM/r/efl18UppV15W6lLfvz7YGHRVcDJKaWvlwcc5Nu5MaX0HrKMDZWmEJuMa7olZNksnpBS+ngx4CAv/yqyKSxKHeodZFNCtLqJ2nf/CIOT/vWTXQd8MpVlJcqPlWeTBZ0sb/RDRMSV1R6Nlqn6rUk3sy6/ZD2Kk5gdldP566Gre1cfn3vddezY3MuSfWbxvH+sPSHOrq29XP+bbCanU//MLAeSJOmhbmgM2F4cwCFx9GCWsJkxi6PjZBbkiUrXUPH2rh7mDo8TWBH7DpsSZlbM4dA4jsN5BABb2MDm+m/b6GHivnQnl/ALeujiGE7jLJ7Ko3kGJ/Fo5rOQddzD5VzAnqaPE5s+mhJ0kCqPsKy27jeBi/M/5wJPbEYdKngQeG1KKVV6MWVzxr4CKNW9jaGUz+XeUHh+HdVTWZN3oryErIMUspF6b6i2fpmfpZS+VOO6zVLzNlNK3cWb/WOseyvZCMGSVus4quY8srT8JW9Mo2QjSCn9kmwE+eD783T3ky6/wV2xvVdY9/dkQTmQtdHz6tjU29Io6YHzsi8pLGqkU6pSuQPlHRajrLuB7OZ9SbX2t3/h+WWVAhmqlD/Rc05/Ju9UGMtkHZu+nlL6VrUX84ClzxUWVf3OJ7GdTjfFoIARQQcRsS9ZRzNkv68fj7Y+2VQCpcmpeoHfNaGOzTAlx48m+kNK6UPVXsx/Zx8vLDo9DwCp5O8Kz/85pfST0TacUrqXLFtUyeurrdugxzCUMr6XbFR2eR02Aj8tLKqaUWAUF+SBAhXlx77bCotmAh9MKf1plDKL5+G5DGVjKPcmGMzL9ruU0jtHq2j+fb4UBkObn5xnyxrLRuD1tR7ranQ+QxnKdgHPTiltGutNKaU9lc5Zk3hN9+H8d12t7NvJMoiUtNpvfoSJ2Hd5EPZfFRZ9KqV04SjlrmPkNBsC2gqJ/IbGG4zUn7/WXmPiv/aayy2NJhy73PvSndzO9QAcynGsjP3HeIcmw8zZQ7dIerurzpBE756sHXTOaTx5ZG93P5993XXcc/0O5i2ZwWs+dzzzFteeyvWqn2ygvzfR1hGc8nSDDlpFe9R2vBhItR8v9NB1Wfo1v00/HPG4O/8v09BIu1HaUuG1etpTaYTfaO20uO1i2RNxXlRjbEOaCK16TV38+4BhSV8pLM8S6e1ix7ApjzR5WrX9jGU/DmZWPmvzRtaOsbYmUqu2oa1pEzdzFUEbJ3E2e8X+dMZsZsRMFsdyTuLRzGU+3XRx++gz+D6kNSvTQb2KHQqnTtA2vpZSGnVu6fyG3XcKi55Vvk4+sqw4j/y/VRrRVFbuvQx1lEE2Gr4WVW/CT6CJ3OZkfM/NVuzUvIfh7aOajzIUatlOlgZ7Omjk+9lJNr3BWC4qPD+66loTq5bPV7z6PG6UzsHJNubvcpKPTf9ZwzrF73x1RNMmv5qOx5FG/Kbw/DH5qPOiYmDBr8mCCEqdXo+tUF5x/UtrDdiZYNPp+FHNf429yrD6zwYOLl8hIk5gqGO8F/i3Grf/bRicUGz/Jmf/KAYQ/DQPMKjky4Xnz46IenM5f6aGdf5Yz3tSNq3RfYVFIzJX5dNUFEfR/2sN9SCldBdDQTtB5d9bua/XE5A7ljwL03MKi76U12syNXosrvc302q/+WaoZd+dC8MmFPxkDeX+ALi30UqllE6q9mi0zFZQnO9xtHkda5mDeHi5xbmOq9+87Mm3OVa5a9Pd3MxVABzMURz40EzmNC0tXDE06mnbhurxyNs2ZklIFixv7JK3r2eAL7zxBm7941ZmL+jgtZ87npUHVUzUU9Vl389uhh75qCXMX+q8s62is5BwqXuUS/CuvCOks636fLJ66Ouhu+KjL7/ZXTr/9Ixy7imel2o9rxXLHu182Z/6B9OZF8tu9nlRjbMNaSK06jV18e85VWYtLC7vGmUbmjit2n7GEhEsYAkAe3j4jlJvBa3ahu4hS2a6jL2YU+F2aFu0sx/ZWKWNrKW5Y5Gmj6aHCUbEcuDxwPFkac0XAJ1lqxVD0fZrdh1yPx17FSAbLVpKJ7xPROyX37wuOb1s/R/WWO73C+Uui4hDU0q3jfYG4Lc1lt1MDW0zImYBjwMeARxC9j3PJrshX7Kk8HyivudmK37fP65llGJKaU1EXMNQB9LpDB/VNukiYgHZ7/AEstTK88lGdxa/n30Lz2v9fq4oT/NbRfE3tKjGsmuWd8CcQzY38mFkadjnMvzzFe/eLImI2WnkFCWXF54fCXw1It6Wd85Ple3ANTWsN1nHpl6G76dqit95kH0n1Tots5Umrp1ORxeSTavRRnbsPAEojux+TOH5BSml3RFxKfAo4PiIWFIWaFcMOmiFqRWgRY4f4/SHGta5r+zvRRXWeXTh+Z9SSptr2XhKqTsibmbofHMycGst7x1NPsq6OAL7S6Os/iNgM9k0CXPJpoj4bB2bu2TsVSjmsbsrlU0ZU8Vaho4Riyu8fmxheWL4dDNjuYah3+DJjB040ezruZOAeYW/v93Mwifwmm5NSun+GtZr5d/8qJq4704rPF+fUrqhynqDUkoDEXEhw6eue9ibW7jJuIvtw/4uSSmxO092N3cwKdDoZkYnM9JMeulhJ9tZSuVR5Tvz2TRGK3d9uo8buQLIRmIdXHWGP02FlQfPIQJSgnW37aoYCDAwkNhwV/bfir0OqX9KjP6+Ab78tzdyw0Wb6ZzTzqs/dRz7HVlfDN+6O3Zx97XZ7DlOrdBa5rYtHHy+c2DrsL9LUkrsHtiWr79osqqmFvSoeMqor5fOY7vYQUqJkbHpQ+eebP3azmsA8/J1e+iiJ3UPSzVdsqtK2c08L2p8bEOaCK16TT2XhWwadrtArahV24+mj1ZtQ6Vz2myq/x+w9NoA/fTQ/bAMmmta0EE+T+6HyUaK11PuombVoUyt+SvK1zuM4Tc/iwES6/K07bW4tuzv1QxPF1xu21iZGSZA3duMiLlkKdxfA3UduRfVs52pkHdkryosqqXjt+RahjqBKud3mgQRsRT4ANloznqOaItqXK/WK7tiOGB9Q3ZGEREzyNIJv5X65zFexPDMBqSU/hARFwNn5IueCzwnIv5I1lF7MXBJKpt3fILdVWNK7sk6Nm2uMY10eQho1e99EtrptJNSejAirgZOzBedS+Wggx0MBYFcQBZ0EPnr/wsQEZ0MtenSeq1gSo8fTTLmZ0gp7Sq7kVPpMxxXeH5gRPysjjocWHje8HzuZZ7H0G9xM8On7xgmpdQTEd8AXpcvein1BR3U0g6KwwJrbTfF94y1z/uA/610w62K4vG2ln1+Z60F1+jIsr+vaEahk3BN91D4zVc0Afuu+Lu+qY7ybqxj3YeFjpjBgrSY7TzIZtazYljsYmYbWwZH3C1hRc1lL2YFG7iPLaznQEZmJuhKewZvQlQrd2N6gOu5lERiXw7msDi+5u1rcsya28H+x8znnut2cPPFWzj+CSMP+3dfs509O7IRpIedXinOrbqBgcRX334z1/xyIzNmtfGK/zqWgx4xslN6LJd9LzvEzlnYwbGPWVb3+zVxOmIGC9qWsn1gM5v7HmBlx4Ej1tk2sHHwOLS0fe/JrqKmkcX5+aSPXrazhYUsHbHOFrL43IUsGTa9x1gWsYwgSCS2sIG9hs0+mdmcl93JrBE3/JtxXtTEsw2pEa16Tb2EFdxNNnXIbnYMjkov2s2Oweezp8d/Lx9yWrX9jCWlxHayrrLROpU18Vq1DUU+vqSLUbKZFV7reJhODdSU6RUi4hTgauAvqD+QYWQYZHPUNGqwwnrldw2Kf486ardM+bpj3Y3YPsbrE6GubUbEMrJRnudT3w1WyOZkbnWLyv5u9Puu785Tk0TEwcBVwCupryMXav8d1jJKuVzNvTqjFhIxG/gJ8CEa62ir9hmfDVxZ+LuNrMP2nfn2NkfE5RHx1ogYeTXbfLX+Lifr2NTIdw5VvvdJaqfTVTE4YDCFe77PVuV//rYwjUbF9cnabynTxx5qG1k+Gabs+NEsNWZqKFfpMxTv9KwEnljHo/ibrb+XorLi1ApfTylVzyed+VLh+SMjorxTvKoayi5X7/ow9j6fQX37/JDCe2vZ582+piuee3Y1Y7qUSbqma/T80dImaN8tKjzfWkd5D9a5/YeFvTgAgHXcU3Ee17vzBDHzWczcOmaIKd1I38x6dlSIR70nL3cmswZv8BdtTuu5jj+SSOzNgRzBI2retibXSU9dCcAVP1rPtg0jD2UXfDFLirb/0fPrmhIhpcQ3330LV/5oPe0zgpd94hgOO63+/zoODCQu/2HWiXPiU1bSMXOqZs1UNXt3ZLNrre27i+6BkaftNT1ZQpsFbUsrZkKQSubFAubll593V0hw1p32sC6fbal0/qtVR8xgGVnQyz3cOiIFcH/q4/48lnYl+48YIT/e86Imh21IjWrFa+rFLB9Mj15Kcz7y/dm4qgUsZmY8/EYYt4pWbD9jjfG7nzsHO4yXVRkBr8nTim2odD7dxLrBqdKKUko8wBogy5JQTyDfQ8m4/3eaj/T5LkM3y3qBr5KNGD6W7EbprJRSlB7A+8a73RrUepO6/C5CeadW8e96bnyXlzvWWW6gjrKbpd5tfpZs2oySC4FXkaUaXkk2Oq2t8D0/ZkQJra38u2/0+570K5p8rupvw+D/EBJZGv0Xk2VgWAbMLvsdvqRCUa3sA2QpjEuuAt5INs3APmRpvtsLn++gWgpNKa0lS2v8UrJ5xMuvQNrI2viHgDUR8erxfIga1Pq7nKxjU9M8TNrpeBSDCM7KM3tA9akSLmEoe0dxneLzPzTQyauJ16yQ6WZcxx3PUIYNgJdExKbRHsDPy4p5Ga1vMvd5s6/pisfp6pPZ1eehfk03kdx3LW5fDmYWc+inj6v5AztTFgfUl3q5LV3LRrJZP1Zz9Ij3/ir9D79K/8MdFWa4WM4+g6OpruUStuUz4wykfu5Otw7e4DyEo2iL4YeKrWkT13AxAwywkv05ipMrpjdWazjzL/dhyT6z6N7Vz6dfcy1rb88SsnTt6uP7H76da36ZxfA+7U0Hj3jvG478DW848jf85D/uGvHadz94O3/837W0dQQv+djRHHXWyNGmtbjlkgfZtj67pD/NqRVa0n4zDmNWzKWfXq7q+jU7B7YC2XHo1u4r2NB/DwCrZ44MPvrFzi/zi51f5vbuqyuW3Zu66Uldg4+SvtQ7bPlAGnk50p/6hq9DPwADaWDY8r6aEt1psqzmGAA2cD+3pWsHv5+daTtX8wf66WM2c9m3wi2QB9KawXPbnjRyfuqDOYog2M6D3MDl9OQx1l1pN9dwCV3spoMZrOKIEe8dz3lRk8s2pEa04jV1W7RxKMcCWUfkHemGwfbck7q4MV3B9jwu+2CcwmwqtWL7uYWruSVdzda0if7UP7i8K+3mtnQdt3A1kAW3LAszUU21VmxD+5H9/6+fPv7E79iSNjCQBkgpsSvt4BouHjwG7T91ydCnXDNCLV7C0PykvcDjU0oXjfGe+iYsbMx8ahv9Uz5CaVvZ31vLyqxVeblbK600XUTEMcCfFRa9I6X0wTHeNhnfczNtLfu70e+7vJzJ8BSyOZ9LXpBS+voY75k2309ELAb+X2HRp4HXjDENQc2fL6XUD3wR+GKe+v8s4EzgHLL9WrorPB/4ZERESumTtX+CCbG18Hy6HJtarZ22T2DZjfgdWdr3DrL5208lG01bMeggT3P/B7JgnMMjYt98/vRqQQpqHVsLz3+QUnrmVFWEkQED8/JHPV4YEW+vcSqWqbK18Hx7Smk6DSssXs+Ou94Pk2u6CTGB+25r4fmiOqo0Jdm1Wl17tHN8OoOr+C072Mof+QXtqYN++gbXWc0xLI36OmsjguPSI7mSi9jDLi7nN7SnDgboJ+Uxq/tyMPvGyI7oO7hhsHNvC+v5HT8aGeaaO4wT2CtGpifW5Jk5q51X/Oex/MdLrua+G3fywadfxqx57XTv7icNQEQWcHDkmbUnQdvyQBcXfSWbxTECvvXeW/nWe0eOOC35p9+dWfW10tQKKw+Zw4HHOVdtK2qPDh4x61yu2PMLdgxs4eLd36eDGfTRR+nHf+jME1nWMTJV7Fgu2f1Duip0/F3bfdGw8PKTZz2RJR3Dj3N39VzPnb0jZ5Hc0H8PG3bdM/j3Ph2HcMysR9VdN02MZbE3B6ejuZMbuJvsZnhbah88r81gJsdzBm1R/39v58cijkwncRNXso57WMc9dKQZg+mK22nnOE5nZoxMOjie86Iml21IjWjFa2qAveIAdqZtrOEW7uIm1nAzHWkGvYXxWIdynJ3GU6wV208/fazlbu7Ns2R0pBkk0rA6LWIZx3F6Ix9ZTdaKbWhRLOPQdBy3cS272M5V/JYgCNoG/7+fvf8g9nsYn7uaEXTwpMLzb9QQcABUmOSp+Q6itqCD8m9/fdnfxXnSD4iIjkJq69EcUvZ3rfOtt6ri97wG+Oca3jOt7pbl83DvZmg+4fLvcDTFdafiuy5+P7+toSMXptf381iydNiQzdv9ljECDqDBz5dS2gx8L38QEXsDrwDeztBo0w9GxJebkeJ6HKbjsWki22lxNP+MqmsN11KdNSmlnRFxOQxe3Z5LFnRQGiW7CSi/S/hrhjKAnBsR/wecUnjdoIPWVJzrfuVUVSIiOoHnN6GoFcDTgP9rQlkTpbjPF0TE7JQq5EJrTWsLzzsi4uCU0p3jKO8hf003gSZq391deF7zdCXg8J1q5sciHpmewBpuZhNr6WYPM+hkIYs5gENZEo0demfFHE5Lj2MNt7CB++liF+10MJ9F7MchrIz9Kr4vFSIMesdIUFW8WaGps+8R83j7D07hl5+9h+sv3MS29T3MXTSDA49dwDkv2o/DT69v1rXif136exM7NjWWiGrPzj6u/VWWaeHUZ5rloJXNb1/CGXOeyV2917Gx7166025mRCcL25Zx4IyjWNphZ4hqd3AcycK0hHu5jW1sGRyZvoy9WcURdI4jhfg+sYp5aQF3cysPsoleeuhkNktZySqOYE5Uj0du9LyoyWcbUiNa7Zq6ZHUcy+K0nHu5g+1soZeePBX6Mg7gUBZGY9mk1Fyt1n7242Bm0slWNtPF7vz/ZYlOZrOAxezFAaxgXzPStZBWa0MAB8ZhLE7LuJc72comutlNGmxHS9iXg1hWZyDEQ00zgg4OLDy/bKyVI/vVntGE7Y7lNLL067WsV9IDXFf2enGu91lkKYjH/JwM/4z9kOdnaZ5irrzJOBIWv+craujwBWh2aPxkfOYryUa5Q43tNCI6yEYkl1zR7ErVoK7fYW46DV0ofr4bU6owtGOkpny+fPqF90fEA2QplSEbaXoa8Juy1Sfzd9mqx6bRTGQ7Lc6hXutd4GNrXG8yv9cLKAQdRMT/wuBEYhdWOPYWgwrOJQtMKAVdbKd5x6PJPuc027D8snm2klrOYxPlYuB1+fMTprAD/M8Y/ns5KKW0ptY3R8SPgKfmf76U1g46uKTs70cy8hjeqsrrfg4wnqCDVrimm64mat9dWni+MiKOTqlCHsCCfMqic2oo+2GrM2ZxOCdwOCfU/J7HxbPHXKcjZrCaYwZTFdfi5Din5nXVOhYs7+RZ7ziUZ73j0Jrf84mbKs+osnTf2VVfq8fseR185E+PHnc5mhydbbM5ovNUjug8deyVc0+Y96JRXz977tjHqWpWd57A6s4TGn6/ptbSWMnSOuOV94lV7MOqMddbEEs4lkc2VK9GzouaGrYhNaKVrqmLlsZeLOXh3bE3HbRS+1kYS1mIASnTTSu1oZIFsYSja+5+ePhpxoRItY4oLXkSUH8Oufr9VY3rPa/w/NKUUvl855cxfP7cF9ZY7l8Xnl+ZUtpZ4/tqVex0nd3ksiup63vOU9T/WZPrMBmfuZip43ERNYUlPRWGnbFqyfbRbPV+P0fBtMoVVO/nm8Hw32Az/G/Z35XaxmT+Llv12DSaiWynxVGix9VQ9knAATWWPZnfazGI4HSyKSlKfl1h/SsZmhboXIZPrfDbfOqQZpjsc06zlQcqTfVn+BVQmoqgE3jBFNWjOLXCpfUEHOS+VXj+5DwzTEtKKT3A8EwhL5+qutQrpbSe4QFirx5nka1wTTddTdS+uwAoBh69pob3PAMzUEiSJEmSJKlFNCPo4IHC87NHWzEi5gAfa8I2a3FWRDx1tBUi4i/JRgeXfL58nbxD7huFRa+MiCPGKPeFwCMKiz4zdnXrVky1uzwiJnpu4uL3fHo+un80H6P5nTqT8Zk/z9CI2BnAv4y2cp6aujiX793ALyagXmOp53fYBvznxFan6Yqf79iIGCst/ruoIbgp6suXVJ4PbkuFdYpt9JA6y69LCx+bRjOR7bQ4ov8pEdXz9+Xfy1hzcBcVv9fah7o15mKGZmPtBN5SeG3EVAl5UMFv8z8PYHjndTOnVpjMfTAR1pb9PaWfIaW0EfhyYdE/RkStQTBNkW/vsYVF36q27ii+z1DwUzsw+tC8qffhwvPnRsRTqq7Zej5eeH5KRLx+HGW1wjXddDUh+y6ltJXh5/RXR8Q51daPiJUMbxOSJEmSJEnSlGpG0EGxU+PZEfG0SivlI31+BBzehG3W6isRcUqlFyLiLOBzhUW3U/2G+78wdFN9JvCjiDi4SrmPBz5dWHQb8LV6Kl2jaxkaJQnw5gnYRlHxe94X+MdKK0VER0T8K7WPuq7HhH/mfJRnsSPoryPiXZU6jyNiLlmbKc69+w9NHFlcj+L3c2pEVBwhlwf+fJXpl473QhicDLcT+I+IaC9fKTJvAt5ZY7kfj4gPV/s9F8rtYHgAShcj013D8CkPlgAvqbEejWrFY9NoJrKdFjNRLKJKZ0yeBeNTwOPrKLv4vT4hImqdlqFuKaXytlXKqHFfSunWKm8r7te9qiwfr+I+OD4iHtfEsidcPsp9XWHR31Q6hkyy95FNhwGwArgoIsbMRxkRKyPi/IgY7+/3JQxdBybg2/UWkFLaDvy0sOil46zTRPsG8If8eRvwnYh4yVgBYhExJyKeHxFXjrbeBPs6w3+HH4+IN+UBWhVFxLyIeEt+vVLUCtd009VE7rt3Alvz5+3ADyPi1XmAa7Hsc4HfkU31sLGO8iVJkiRJkqQJM9bonFp8BjifbBRwG/D9iPgK8ENgPbAYOIvsRvRSsjmmf0zt0x806hv5Ni7O6/NjshtzK4GnAc8nu6EH0Ae8LO/sGSGldEtEvBX493zRIcC1EfEFsnTXDwL7kKVPfQ5D8133AC+oVu54pJR2RsT3gdIEJe+OiJcCNzI8Pes3U0rfbML2fh8RlwGlyQjPj4jTyDro7yQbxXU8WSdGabT1pxh/CuBiHSbrM/8N8Gig1Hn7fuBpEfEl4BayDAgnAq+EYRObfS+lNCJbxiT5DvABhtLs/ldEPIGsE+k+YD7Zd/fSfJ1e4L8Znlq7ZaWU7omI75D9viCbFuXIiPgMcBPZd3Ik2c39UqBRLe1vIdnI3L+NiCvIpsa4muzYtZus8/q4vNxicMnH88628nrekpdzcr7o8xHxdrIO/p7Cqp9IKY27Q7gVj01jmLB2mlK6NSL+F3hWvuhlEXE4WfaSO4E5ZL/bl5LtpweAG6gt+OB/yYIYZuXlXB0RV+dlFIOMXplS2lBDeWO5gJEBF6O1l0qvbSIL1GqWC8iyBexN1o5+GRHXA/cwPBjsnSml65u43Wb6KvC3+fMXk2XEuA4oTjFyQUrpE5NRmZTSfRHxbODnZMFUq4BLIuIC4Cdkx7ZtwFxgOXAscCZwBtn1VsNT+eSd7MWgqN+nlO5vsLhvAeflzw+NiLNSSr9rtG4TKaU0EBHPAi4l67CdA3wBeGtE/A9wFbCZ7JyymOy4fypZRog5U1LpXEqpNyKeQ1b3ZWRt4KPAKyLim8CfyI7588gyeTyKbPqnuZRl8mqFa7rpaiL3XUppbUS8CPgfsjY4D/gk8C8RcSPZufsQhjI53Up2/i9dA5RPESdJkiRJkiRNmnEHHaSUNuQ3yL6Vl9dG1olXKcXuLuC5wGnj3W4NXgUcBpxEduOv2ojjfrLOt99WeR2AlNJ/5CONPkzW4TIXeH3+qGQHcF5K6bIG6l6rN5F1bq7K/94vfxRd3cTtPZ8s9ffy/O9zqDwSOZGN4LyI5t+gnvDPnFLaHhGPBn4GHJ0vPpWhG8yVfJesI3xKpJS6886IXzPUMfJnVJ5HuJdsruB+pknQQe61ZFMDlNKiP4LsZnwlXyDLAlBP+zuZoWCB0XwNePcor7+CbL72pfnfq/NH0ffqqNeoWvTYVNEktNPXkXX2lPb3o/JHuY1kc2HXlJ48pbQpz8rwWYbOcycyfHoeyAKWmuECsmCn8mXVXEf2mZYXll2YUkpV1q9b3uH5YuD/GPrujskfRR9v1jYnwPvJOo9LU4ysYPj0AjA0ynhSpJQuyjMvfZehc9m5+WMiPZas071kPIF6PyS7viuNpn8Z2SjslpRSWp93En+HLCgWsuCCd01drWqTUrozIk5neOawI8muuerVCtd009WE7buU0g8i4s/JrmNK5S8AyrOg/JEsmLA4ldy2WrYhSZIkSZIkTYRmTK9ASum7wOOAaqMb+8nmuT8xpfTTKus0VUppB9mIwH8juxleyaXAaSmlmuYxTil9hCxg4tfAQJXVushGOx2VUvp1XZWuU0rpPrIOtrfkdVrLUKr1idje7WSdsj8ZZbXrgKemlBq5AV5LHSblM+fbOYUs1e1oqWtvJbv5/OyU0pSOMEsp/ZHspvTFo6x2CfCoKczI0LCU0mayz/c1ho8uL7oTeHFKqdZO6k+TdSSvqWHdq4DnpJRekFLqrbZSSulqsmCV95B1vG1keJaDpmu1Y9NoJrKdppTWkXUifpuh6TiK+skCPk5IKdWVJj2l9CWyILb/JBtRvJUsS85EuIzho+9hlKCDPLjgN7Wu36iU0i/IRtt/iOz8uZnhWQ5aWn5dcDpZUOJPybJr7Bn1TZMgpXQ5Wcfx+WSZI0bTR/b7OJ/xBboVj5H9ZCOrG5JS2k3WCV7y7IiY32h5kyGltJ6sk/i5wBVUPl4U3Qx8BDhhQitWg/xa7ASyrB33jbH6LcDfM/J40hLXdNPVRO+7lFIpqOTtDB1ru4G7820+Dzg7pXQvWQa3EqdakCRJkiRJ0pSJJg6ELKXrPZHsRtxSshG1a8nS9q4b7b1N2PY5FDpdUkpReG0u2ajBA8jSd68HLk4p3TKO7S0HziZLXT4f2ELWcfnb/Ab8Q1pErCL7/HuTdYKsBa5OKd04lfWaCPl8yaeQdSQvJ/u8G4DLU0o3T2XdqomII8lScK8g61RbC1yWUrprSivWJBGxN9kUGKU0/euAm1JKV4yzzGPJsmgsJhvRvpOsE/CqlNLd46nzZJlOx6aJbKf59/kYsjTU/WSdc79LKa0db9nSRIqIQ8muo5aRTQGzh6zT8Vbgujx4Qk2UHzfPJLumWUx2nt9KFsh2/URfw45HRBxDlr1jOVlq/x1kndN/SimNFcRSKmMVD5Nrumab6n0XET8Bnpz/+U8ppXc2o9zHt/1F8/6DqIelZ9y4eaqroGnuR6ccMNVV0DQ3sKva2CNJkiRJtfjlwHdi7LWGa2rQwVQaLehAkiRJkh4qIuJA4HaGpst7ckrpZ80o26ADjZdBBxovgw40XgYdSJIkSePTSNBBU6ZXkCRJkiQ1Ls8aV8t6s8imTCoFHNwL/HKi6iVJkiRJkiSNxaADSZIkSZp650XEjyPiORGxsPzFiOiIiKcCl5JNM1Xy3pRS/6TVUpIkSZIkSSrTMfYqkiRJkqQJ1gY8JX+kiLgLeADoBhYBRwJzyt7z1ZTSFyazkpIkSZIkSVI5gw4kSZIkaeoNFJ4HcHD+qKQL+BfgfRNdKUmSJEmSJGksBh1IkiRJ0hRLKX03Ik4FngScBhwG7EWW3aAL2AzcCFwIfDmltG6KqipJkiRJkiQN85AJOkgpXUg2IkiSJEmSpp2U0uXA5VNdD0mSJEmSJKkebVNdAUmSJEmSJEmSJEmSND0ZdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJkiRJkiRJkiRJaohBB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJkiRJkiRJkiRJaohBB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJkiRJkiRJkiRJaohBB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJkiRJkiRJkiRJaohBB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhrSMdUVkCRJkiRJDw3ffd0TproKmuY+dP0np7oKmubecdCpU10FSQ9z7UuXTHUVNM31b94y1VWQpLqZ6UCSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpAkSZIkSZIkSZIkSQ0x6ECSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpAkSZIkSZIkSZIkSQ0x6ECSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpAkSZIkSZIkSZIkSQ0x6ECSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpAkSZIkSZIkSZIkSQ0x6ECSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpCkgog4JyJS6THV9WmWiHhx4XOtadUyJUmqJiJWFc/REbFqqus0lR6q1yySJEmSJEmafgw6kCRJkiRJkiRJkiRJDemY6gpIkqTKIuIc4Delv1NKMWWVkaSCPMvAXYVFB6WU1kxNbaSRulMXa7iZTaylmz10MIMFLOEAVrMkVjZcbl/qZQ23sIH76WI37bQzj4XsxyGsjP1Gfe/utIN7uJ0tbKCL3SQSncxiIUvYj0NYHMsbrpear7t7B/esuZDNm2+mu3s7He2zWLBwP/bb/0wWL1ldd3kDA31sffBOtm+/jx35o6dnBwDHnvBili49vO4y773n99xx248B6Jy1iNPPPL/uMjRxNm3o58v/tY3fX9DFxnV9zFvQxlHHz+S5L13AqWfOqru8Bzf385uf7eayP3Rxy/W9bFzXR1t7sNc+7Zxy5iye+9L57L9qxqhl3Hx9D9/64g7+dFkXm9b3ExEs36udR5zayV++eD6HHT2z0Y+rCdBK57KBNMAD3MU2HmQnW+mmi166aaOdOcxjKSvZn9V0xuyG66Xmsw1pPLoHdnPn7qvZ2HM33QO76YiZLOxYzoGzj2XpzH3rLm8g9bOl9wG29W1kW99GtvdtpHtgNwAnLngyy2fuP+r7d/RtZmvv+sH37+p/kERir5kHc/yCxzX0GTXxWuk4dEe6gbu4qabyF7Ock+LRDddPzWH7mX4MOpAkSZIkPWTsSFu5it/SSw8A7XTQQzebWMsm1rI6HcOqOKLucrvSbq7kIvawa7DcPnp5kI3ZIx3MEXFixfduSPdzPZcywAAAQRttBF3spovdrOc+DkpHcEgc0+CnVjPt3LGWq//0Ofp6sxvh7e2d9PbuYvOmm9m86RYOOuQJHLjqnLrK3LVrA9de/cWm1bGraxtr7vxl08pTc912Uw+vfd4Gtj2Y/ebnzg+2bhng97/u4g8XdPHaty7kRa9dWFeZTzntfvr7hv6eMzfo7U2suaOPNXfs5Aff2sU7P7SEJz5zbsX3/89XdvCR9z5If3/2d2dnFs9839193Hd3Hz/57i7e9g9LOO958+r/wGq6VjuX9dLDzfxp8O8gBt+7g63sYCv3cSfHpdNZEisa/NRqJtuQxmNH32Yu3/YjelM3AB0xg57Uxcbee9jYew+HzjmVg+ecUFeZO/sf5MrtP224TtftuJAd/Zsbfr8mX6sdhzroYCadVctNQC9Zm5/Porrrpeay/UxPBh1IkhqSUvoS8KUproYk6WEiz6RgxheNqj/1cw0X00sP81nE0ZzCvFhIX+rlTm7kHm7jdq5nflrE0tir5nJTSlzLH9nDLmYxh2M4lUWxjP7Uz73czu1cx33cyfy0iH3j4GHv7Und3MDlDDDAfBZxBI9gAUuICHanndzOdWzgfu7iZpaklWY8mGL9/b1cd+1/09e7m3nz9+HIo57D3Hkr6evrYs1dF3DfPb/jrjt+wfz5+7Bk6WF1ld3RMYt58/dlwYL9mL9gP2647msN1/P2W39Af38P8xfsz47t9zZcjpqvq2uAv335RrY9OMDhR8/gvR9byiGHzWTnjgE+/4ltfO2zO/ivD2/j8GNm8sizax/R298Hjzi1k2f85VxOO2s2y1a009+fuP5PPXz43Vu49cZe3vuWzRx82AwOPXJ4xoI7b+sdDDg47axZvOndizlodXZL8I5bsteu/GM3H37PFk45s5P9Dhw9Y4ImViuey9poY39Ws5jlLGQJM5lFRDCQBtjCem7lWnazg+v4I2ekJzEjzJoxlWxDGo/+1MdV239Ob+pmfvtSjpv/GOZ1LKFvoIc79lzFmj3Xctvuy1jQsYxlM0fP9FWuI2ayoGM5C/PH1TtqD6CMaGN++9Ls/TOWs777Ljb33lfvx9MkacXj0IFxOAdSPbvYhnQ/13IJAPuwqqHPreaw/UxfbVNdAUmSJEmSmuF+7szTI3ZwPGcyL7KRxB0xg8PieJazDwC3c31d5W7kAbazBYDjOYNFsQyA9mhnVRzO/mTp9u/gRgbSwLD3bmIt/fQNvndhLCUii5+ZE/M4htOYTTayeAP3N/Kx1UQP3H8p3V1baW+fybHH/TVz52VpOzs6ZrH60KewbPlRQOLOO35eV7nz5u3FmWe/mxNOfDkHr34Sy1c0ntVi08Yb2bTxRpYtP7ruwAdNvP/72k7W3t/PnLnBRz6/nEMOyzrO5s1v441/v5hHP2E2KcF/fWhrXeV+6lsr+PS3V/LUZ81j2Yp2ANrbg+NP7uTfv7KCJcva6O+Db3x+x4j3/uqHu+jvzzIu/MunlnHwoTOICCKC1UfM5MOfXc7ceUFfL/zuV3vGvQ80Pq14LpsRMzk8TmBF7EtnzB48j7VFG8tib07gTCAbzb6JtQ1+cjWLbUjjcW/XTXQN7KQ9ZnDigicxr2MJAB1tMzl87iNZMXMVALftuqyucue3L+XcJS/ilIVP5bC5p7Ky86C63v/Ihc/kjMXP4pj5Z7P/rCPpbJtT1/s1uVrxODSWtdwNZKPUS/XV1LD9TF8GHUiSJEmSHhLWcQ8Ae7E/syrMCXwgWQftDrayK43smBur3CWsZH4sqlBuNuKhhy62sGHYaz10ATCDmcyKkTdH26KNeWQ3Jfrpr7lOmhgb1l0NwIqVJ9A5a+TNov0POBuAnTseYPeujTWXG9E22MEyHn193dx2yw9oa5/J6sOeNu7y1Hw/+342LccTnzGXFXuNTDD6wlctAODm63u5+47emss98bRZVV9bvLSdM86ZnZfbM+L1zZuym6b7r5rBnLkjbwXOm9/G/quyuu7Zk2qukyZGK57LxjIn5tFBliGjGwNXppptSOOxtvt2APbuPIRZ7SOn7Fk1+zgAtvdvYlff1prLLQW7NSrCrqzpZLodh3pS92DA094cWPP7NDFsP9OXR2pNiYiYGRFPiIgPRsQvI+LuiNgVET0RsT4iLo+Ij0fEKRO0/XMiIpUeheX7RsQ78+2vj4g9EXFXRHw9Ip7Y4LYWR8TrIuJHEXFnROzMP+tdEfE/EfHXETHmVCej1HmfiHhbRPwhIu6LiN58nXMqlHFQRLw3Ii6MiHUR0ZWvvzUiboqIH0bEuyPi5Bo/W3tEPCcivhoRt0bEtnyf3R0RP4uIN0ZUOHpXLutLhc/3pcLy4yLiExFxQ17+roi4LSI+FxGPqKXsyRARZ+b1vDYiNkVEd0SsjYhLIuJ9EbG6lcqtYbuH52209J2sLd/fEfHiwutrRinrvYX1LiwsPzgiPhARV0fElrztrImIr0XEYxqs879GxPV5W9kRETfnbevswnoV21ozRMRRebv/dl6PrYXf2G0R8Y38Nz9qvtJSHYHflC1PVR7j/hwxxcflCvXpjIgXRsRX8u9xc2FfXhMRX46IF0TEqJPORsTJEXF+RHwvIm7J20Zv3uZujIgvRsR5UeP/Xkdpz8fk++f6iHgwsuPrzRHxsYjYt0pZT8nbyj35fn4wIv4YEW+JiOqThFWv274R8daI+FX+/e2OiO152/tK/jnH/B9+td92RKyO7LhzeWTHhL58nVUVymjqfh+PiFgZ2XnygsjOk135vr4pIj4fEU+to6zi7+6cfFlHRPxFZOfQu/LyN+Xf5TsjJia8eap+s6XfAHBX2Ut3ReXj04Vl719V9vqqfPniyK6XLsg/S1f++ovHem+FOk7mdV7Tz9MRMSMiXp5/rw/k++KeiPh1vtwhPVX0pV628yAAS6mcYnEhSwdvaNdzE+FBNublrqz4+qyYzVwW5OsOL3cW2c3aXnroSrtHvHcgDbCTbQAseBjP/dgK+vq62bHjAQCWLD204joLFu5Pe0fW+fvgg7dPWt1K1tz5S7q7t7Fq1bnMmrVo0rev0e3aOcDN12Wd/o98dOUggWMeMZN587NLsssv7mrathcuzi6r+ivELu2zX5YZ4d41vezeNXLU1s4dA9y7JsvIcsTRpjSfSq16LhvLrrSdPrIgmtmM7KTU5LENaTz6BnrY3pd9z8tm7F9xnUUdK+nIp7/Y3GuWLo00HY9D67iHRCII9uKAmt+n5rP9TG9jdnRKzRYRTwP+G1hcZZUV+eNk4I0R8X/AS1JK2ya4Xs8CvgD5UWXIqvzxVxHxXeDFKdUWPhURbwLeDRXvHpbKfRbwzoh4YUrp0jrr/DzgkxXqXGnddwHvBCrdQViYP44Anga8LyKemlL6ySjlnUK2vyrlBT0gfzwReFdE/G1K6Utj1bGs/HbgfcDbGRkgtTp/vDQi3pdSel89ZTdTRKwAPgc8vcLLe+WPRwJvj4h/B85PKfVNVbm1iIjTgR8CS/NFtwFPTCmVdzCNZxuvBz4MlHesHpg/nhcRnwVek1Iac8hfRPw92W+tvH0fnj9eFBGfA14/3rpX2X4ncCVwdJVVSr+x1cBzgX+IiOellP4wEfWpV6sdlyPihcAHgUqd9QuB4/LHXwM7I2LvlNLOsjL2By4CquXrW5w/jgReDNwQEX+RUrqpzroG8PfAe4H2spdL7e8lEfH4lNLl+XsWAl8HnlK2/iLgtPzxkoh4XEppXQ116CA7Xr4JqDQx8HyytvcC4MqI+KuU0m01fcChbbyZ7DsZ9S70ZO33WkXE3wLvAcqDUzrJ9vcRZOeSS8jO77fWWf6BwDeA0yuUv5Tsu3x9fk69ov5PUHW7LfWbHa+IeDzwZWDvCdxGU6/zJvD8fxTwLUZeX+2fP84F3hIRfzFWWQ9Huxj66uZWuTyOCOakeWznQXaxvaZye1IXvWSdiPNGueyexwJ2sX1EucvZm5nMoocuruFijkiPYAFLiAj2pF3cxnXsYSdzWfCwnvuxFezetQHIYpXmzq18IyqijTlzlrFj+33s2lVfh8p47djxAPfddwlz5qxgvwMeNanbVm3W3N5LysPdDj60cpxxW1tw4MEzuOGaHu66rfZMB2O56tJuAA45bOR2n3TeXD73ie3s2pE4/9WbeNO7F3PQ6uyW4J239vKv732QXTsTp501izMeU+lyUpOlVc9llaSU6KGLB9nEHXl641nMYVme8lhTwzak8djZv3Xw+bz2yv/djAjmti9kW99GdvY/OEk103QynY5DJaXU+MvYm5n1jwNSE9l+pjeDDjQVVjH8Jvl24HZgG1mHzd5knSOl0ZjnAQdHxOkppQnJr5WPbvtOvs0E3AhsILthfGRh1T8HlkfEE0erS94B9HmyDrGiuyHP4QKH5uWXnv8mIp6RUvpVjXX+c+Br+Z8JuAlYDywh60QprvtO4P1lRdyb16eLrDPmQIbf7K86+jTvHPg/GBZ6vItsv3WRfX+lspYCX4yI/VNK/1DLZ8v9B/Dq/PlO4AZgD1lHVilHTQDvjYi1KaXP1FF2U0TEAcCvgeIoxn6yum4h6zAtDZGaAbwZODIizkspdU92uTV+pmcA32So4/Iy4KkppU3jKbdsG39H1nkJ0A1cT3Yc2I+hzwXwCmAT8I4xyvtn4PyyxWvJjiszyX7DC4CXA3OA5t3ZGzKD4QEHfcAdZPXvIjvmHZFvH7KgnN/kHdEXVSjvOuDnZL/n4ijlapMHX9d41YEWOi5HxMeAvylbvAe4GXiQ7Hi1mmzfkP9d6XpmIcM7vrvJPtMWsjawjOw7KXWiHw38MSJOTSndUkeV30sW8ALZfrsx39aRZJ2+pbr8IiKOBTYDv2Toey211XbgeIaOq0cD38/3cdVJxCLL9PC/wBPKXroNeICsbR7B0P46Cbg4Ih6bUrq2lg+YBxx8JP+zn+w3uyX/fEeUrT5Z+72Wen8aeGXZ4vvIfptzyTp1S0MQTwf+kJ/fr6pxEyvIOspLIcz3AGvI9vlxDH2XK4CfR8TRtQSR1GgVU/ebvZ3sWDQbOLuw/LdQMQ/qWO3skWT7sdQmbif7nhZAntdunCbgOm+izv+Hk2W4WVFY3EN2jN9J9ts6gOw3dAEjj5UPe90MjRjuHPx5j9TJbODBYevXXm71zriZ+TbLy22PDk5IZ3INF7ODrVzObwjaaEtBP/10MIP9OITVHENblMevaTL19Azd4JrZWf1GVGfnAnYAPd21p/Icr5QGuPWm70Ia4NAjnklbm22lFW3aMBQvvWxl9e+o9Fpx/fG46Be7uena7Cbq0/9i5AjhlXt38KFPLeOdb9jEpb/r4rmPX0tnZ3aZ0N2dWLKsjZe8bgEvf8PDd/7ZVtGq57KiG9MVPMCaEcvnsYjjOI12z2VTyjak8egZGMrK1dlWPcFaZ9tcYCPdhfWlkulwHCrambaxg62AqfFbge1nenN6BU2VP5HdKD00pbQwpXRSSunclNKjU0qHAfuQdUqWRoQdD/zTBNbnK2Q3or8PHJRSOiavz1FkN5R/Wlj3LOADY5T3jwwPOPgi2WddlVI6O3/sTTZarTS6czbw9YiodZTfl/J/Pwvsm1I6Oq/zCWT771oYHIn3rsL7fgIcmVI6IKV0Vkrp8Sml01NK+wAryUae/o7SEJ8ykaUJ/yZDnSldwFuAFSmlU1NKZ5PdbH8K5CFemffnIzNr8VSygIPNeX2WppQemVJ6TEppFfA4GJbf5kMRMam51/JMDN9geIfD58i+i+Pzuh5G1qFW7FR+MqO05Ykqt8bP9ErguwwFHPwEeEwzAw6AY8l+P3vIRmUvSSmdnLfdw8g6RIsjsN+ajySuVucnMzzg4E6yDBv75r+zRwLLyToedwDPY+To8mbZAnwceAwwN6V0RErpUSmlx6WUTiIbVf0cGPyf9Qyy3/yIq5yU0kdSSk8C3la2/ElVHh8pL6MBU35czkel/01h0d3A88nayYkppcemlE5LKS0l+w28HxhtMuUHyI7HpwHz8mP72Xk5x5N12paCWyDr5Px6HVU+juz4uhV4CbAsP56eQ9aZ+SoYnJx7EVkAzUfJAg5uBM5NKe2T1+lMsmNwMYDq1Pzzj+azDAUc9JNlENk3pXRYSumcvNzlZJ3OD+TrLQO+U+NxcwXwL8AA2W93eUrphLxtHEPWAbq+7D0Tvd/HFBGvYXjAwY3Ao1NK++f75RSy/f1+hr6jZcD/RMT8Gjfzn2Sf/yLgxJTSgfnv5Yy8rOLvYwlQT+BdLabkN5tS+mp+fHpR2UsvqnJ8elulcgo+QxZw8L38sxyan+tOIvuOfjbeOtPE67wJPP935OWWAg4S8CFgZX6ePCeldCBZoMdNZL/rj4/5yR9mBgabO7SNSD4zpD1/rb+w/mj6ay63o2q5C2IxJ3E28/N4ocQA/fnhZ4AB+ugdTCmsqdPf3zP4vK2t+hiNtrYZI9afaPff90d27LifFXudwOLFB0/adlWfPXuG/hvdOav6rFazZmev7d5V8b/dddmwro8PvmMLAGc/bjann1P5Jurp58zm37+6gv0OzNp2d3eiuzvbfk93Yuf2Abr2jL8+Gp9WPpeVdDCDmXQOpjWGrLP4CE5gTs2X0pootiGNR18hOVvbKLMBt+ev9Tcn6aoeYqbDcaioFAQ1g5ksm7gEjKqR7Wd6M+hAU+FLeefRv6WUKk6CmVJal1J6B/DCwuJXRsSiCarTcrJUtuellIod5aSU7iBLnfujwuI35OlvR4iIRzK8o/CVKaWXVvqsKaXfkI2uLAUeLCebAqEW84H3pZRemVJaW1bulpTSlvzPJzA0evAuss94c6UCU0obUkpfzgMHqt3k/zBDI2YHgD9PKX00paEJalPmp2Q37ouTe306xpjLPreMrBPvzLw+w+7mpZR+TTYtRclCstGJk+llwBmFv/85pfSKlNKwzreU0o1k38GvC4vfnI94nsxyRxUR7wc+zVB6+C8Czyx+r02yhKwD7AkppY+Xl5+yEcZPIRsdDdkI9vKMIaU6B/CJwqJ7gbNSSr9IKQ3eLUsp9aSUPksWzNJH9jtrtt3A/imlN6WULixvs3k9elNK3yHriC1lPNmHLOX9VJvy43JEHMZQBgyAq4CTU0pfTymNCC1NKd2YUnoPWeaTSsMMbwNWpZTelVK6LFVIa55S2p1S+hxwJgzmzDoxz+ZSi8VkgVfnppS+lFIa7C3Kj4OfKftMLycLRLiJ7Pj2m7L67CILuLq4sPgl1TYeEX9JNl0HZFkEnpFSeltK6YHieimlgZTS98hGlJeOJYcBr63hM84m+x2+LKX09ymlYbkTU0oPpOEjwidjv48qIhaTnatKbgQelVL6bVk9tudtqBiccBBDmSvGsoxsKprHpZT+VFZ2V0rpnWRZj0qeWynIqEFT/pttovlkgZR/Xv5ZUkq7UnOyQzTtOo+JO0+/EnhE4e+/TSmdn1LaWlbu78gCD25nHOeziLiy2qPRMjW6+9KdXMIv6KGLYziNs3gqj+YZnMSjmc9C1nEPl3MBe9Kuqa6qWlB393buuuMXtHfM4pDVExU/q+lo964B3vrKTWzZNMDe+7bzzg8tqbruZz66lRc/Yz0zZgYf/cJyfnHVvvziqn356BeWs3yvDr7z3zt5+bPXs31b1SRbEgCHxfGcHU/nnHgm5/BMjuE0+ujhCi7k1nTNVFdP04BtSFKrSCmxjnsB2IsDaAu7TFU7289I7gFNulQ27/YY636Toc6XuWQjmCfCg8Brix2VZfXoJxuVWap7G0Op/8v9HUPpjL+Sd3ZWlbL5lV9VWPTiGkdZ3kBtoyb3Lzy/rFJnaJV6jcjzmGdheHZh0afz4IJqZdwLvKGwaB+ykd61eFsaJdV2Sun3wCWFRWfVWG6zFD/XdQzPJjFMvs9fQtYxDVn7eEOV1Seq3Ioioj0iPle2nQ/kgTITFa784fz7qyjvdPpuYVG17/bxDB9p+ubyztaycn9HNjK56fJO3ZoCNFJKG8hGgZdMdsDMCC1yXD6foWkSdgHPTjVk2Ugp7al0vEopdReDAMYo41ayKV1K6vlO/rm8w7nMpwrPZ5D9Tl9V3pFYqEsC/quw6JH5yOpK/q6sHj8ZraL5MfmthUWvH239gp+llL5Uy4qTuN9H83KGT//z0vJgibJ6fIEseGDw/TVmgdgBvHiMY+WHCs/nMbxTuWEt8pttlo3A66tdgzVJM6/zJuo8XQwC+iPwsVHK3QS8ptrrD2dthdl2BhhxahhUyjDQXuNsg+01l9tXsdytaRM3cxVBGydxNnvF/nTGbGbETBbHck7i0cxlPt10cfu4Z0zSeLS3zxx8PjBQ/fA+MNA7Yv2JdNstP6C/v5uDDn48nZ2OAG1ls2cPZTfo7qp+aitlFJgzt3o2hLF0dyX+9hUbuenaHhYvbeMT/72CRUsqXzb+7Hu7+NwntrNkWRuf/vYKHnXubBYtaWfRknYede5sPv3tFSxZ1sZdt/Xy5f/a1nCdNH6tei6rpiNmsFfsz8k8hnY6uIfb2JDuH/uNmjC2IY1HRyG7wcAo/9UtZThoHyUbgh6+ptNxaDPr6MnT6JsavzXYfqY3gw40HRQ7lk+doG18LQ1lBqgoH2n3ncKiZ5WvExFLyEbLlfxrLRvPO0Pvyv+cQ5b9YCyfq9TRVkFxBOpxo3Re1eLpQDFTQS0p3f+PLOV9yXk1vGcn8N81rFdMW3x0Des3RT4iu7i9fxurgz7v7PtWYdGfTVa51UTEHLJU0y/LFw0Ar0sp/X2tZTTov8ZepabvtjhdxzqytjaWCQk6aMBkHNcmUlPrn6cVLwYkfSmldFe19SdIo5/pM6O9mFK6H/KQ18zN+TF/NH8sPJ9NNvp+mIg4ATgh/7MX+LexKpr7NgxOSrZ/ftwZy6drLLsRE/FbKJ5nfp9SurSG9xTP14vIpkkZyzdruHa4lez4VDJp56oyrXzM+Xo9QRQNatZ13kSd/w8vK/c/xgrCSCn9iqFMWXVL2XQcFR+NltkKivM9jjb/Ynd+eTza/JDDyx1KUtI97NJ6uNLNhvJy7yFL4rGMvSqmDG6LdvbjEAA2spaJjcHRaGYWOvR7urdXXa87f23mJAQAPLjlDjZtvIE5c1ey194n0tfXPeyRBob+S1haNjBQy38TNRGWrxz67/am9dW/h9Jry1Y09t/z3p7E3712I1dc3M38BcG///cKDjykelLBb34xSwz2lD+fy6LFI7e5aHE7Tz4vi7n87S+rH+c08Vr1XDaWWTGbFewLDKUZ1tSwDWk8OtvmDD7vHqg+tqZ7YNeI9aWS6XQcWpvPDj2XBSyIxTXVQxPL9jO9GYqmKRURy8lGKx9PNgp+AdBZtlpxJPN+E1SVqqP1y/yYoVTX+0TEfiml+wqvn8VQMM+GlNK1ddThGoY6lk4GfjHG+r8d4/WSywvPjwS+GhFvy2+C16sYDHFTnpJ4VCmlFBE/YGie9loCKq5IKXWPvRrFfb+ohvWbpfwz/LDiWiN9n6H2sywiDk0p3TYJ5Y4QEcvI2nOp86kLeH5K6bvV39UUa/JO2LHU8t2eVnh+US1BOCml2yLiXoZnAGmqvAP9HOAkshT2C8lGFxeHMRVTrC+JiNllKeqnzBQdl08iGwVe8u0mlDkoImYBjyMbZX4I2WeazfDvpJiLttbPdFeNqd/XMdTmLhltxdzasr8rXbU+uvD8TymlzTWUS0qpOyJuZihg4WTg1jHeVuv5ZpgJ3O+jbXMmcGJhUa3H0d+RjYYv7evTGZ5uv5I/1Fj2fcBe+fNFNb6nZi10LdWohtpXnZp1nTdR5+nTytavp75H1rjuw8JchjqAd7F92N8lKSV250kt5rKgpnJnRicz0kx66WEn21k6+JMebmc+Y0x5ubvy5bOpnkSl9NoA/fTQXfeNejXHnDkryE5TiV271jNn7shZTFIaYPfuLBnT3LkrJrxOXV1bAdi9az2/v+h9Vdfr7trK7y96LwCHH/ls9t5nWscQTVsHHjKDCEgJ7rytt2IgwMBA4u47s2wZBx1ay+yDw/X1Jd75hk384YIu5swNPvbFFRx29OhZN9bcnm1vn/2r3wbc94DstbX3OT/3VGrVc1ktSjfz9zDR8aQajW1I4zG3fdHg8539DzK3Y9GIdVJK7OrPsuLMa7eTTSNNl+NQb+phI1nS3H0cpd4ybD/Tm0EHmhIRcSDZfMvnUV87XDQhFaLmPKbl6x3G8M7R4wrPZ0XEz+qoQ3GO31rm6L1z7FUgpfSHiLiYofmHnws8JyL+CFxAlnL5kmqpvssUOy3qmWStGHyxTw0drLXO31yc9HYyQ2uL+2Fdni6/FuVBKKvJ5j+f6HLLzSf73g/N/94KPDOVzXc+QZr53RbP5vWM9ryRCQg6iIgZZME1b6X+ebYXwSghlpNgio/L5Z1mVzShTPIU+e8iS0Nezx2PRTWuV2t7Lg4PGPM9KaXdEcNS7Vb6DRTPNwfWeb4p/nbGaqvbxhohXm4S9vto9md4h3tN56o8QO464Ox80erR1s9N6bmqBa+lGlXT9cw4Nes6b6LO08WMIw/U8ZszD3+ZjpjBgrSY7TzIZtYPjpYr2sYW+sg635ZQe4fxYlawgfvYwnoOZGSSmK60ZzC4oLzcyGOtuqg+Wqz4Wof/TZ8yHR2dzF+wLzu238eDW25n+YpjRqyzffu99Pdlo18WL67ldKGHk7nz2jjyuJnceE0Pl/6ui8c8aeSp//o/9bBzR5bR5JQz6gswGhhIvO8tm/nNz/bQOSv4188u57iTymMNRypNL7vu/upx2qVggznzTIo6lVr1XFaLPfllb61pjjUxbEMaj462mSzoWM72vo1s7r2PlZ0jEi+yrW8DffnsvUtnjGxf0nQ5Dq3nXgYYIAj2stO4Zdh+pjfP4Jp0EXEK2Sj+RQ28fez/TTemphGiFdYrD+dcWni+gMbnTV5YwzrV832O9Gyy0Xil4S5tZEEIpUCEgYi4imx08edHudld/Lwb69h++bqLGb2DtZYsB+Uanwyzfs3cD5NRbrklDB9d/PFJCjiAxr7bahYVnm+t431V53ZvVETMBn5ANqq7ERN1bKtJCxyXi+1xV0qpeq9MjfJsHr8iG/1dr1onaO5poOxG3lPp+FY836xk4s439ZxrJmu/j6b8+NfosbSW4RpTdq5qgd9sM9XVxhrUrOu8yTj/11rXetd92NiLA9jOg6zjHg5OR9IZs4e9fnee3GU+i5lbYaqD6uXuzwbuYzPr2ZG2Mj8WDXv9nrzcmcxicdnNiXksZCfb2MQ6utIeZpXVKaU0mEZ4LgucG3eKrVh5PDu238f6dVdz4EHn0tk5PH7u3ruzWZLmzd+3YiaEZtt7n5NGzVpw152/4u67fk3nrEWcfub5E14fje2Jz5jDjdf08PPv7+Llb1w4YgqFr302O/UdcezMUadEKJdS4gNv38LPv7+bGTPhQ59axsk1Bi0ceuRMrr6sm1/8cBcvff0C5swdHliwe9cAv/xRdgl+9AnNuCTTeLTiuWwgDdAW1QNSdqcdg6P9FrGs5jppYtiGNB57d65me99GHui+nUPmnDRiCoW79mSx/Qs6llXMhCBBax6HypVS4y9hJZ1hprlWYvuZvgxf1qTKRz9+l6Gb5L3AV8lG3x9L1vE0K6UUpQdQPYdk89TaCVTewVB+4756ztT6jPnbTCkN1FpYSmktWerel5LNFV4+UWwbWYrtDwFrIuLVVYoqft56Os7K99t0PwpP1H6YrP27geHTbrw7Il5Wx/Y00gcYHnBwFfBGslTc+5AdG9oLx7WRoeJTpEWOy8U2W32yrvp8luEd3xcCryI71q0kG3HeVvhMj2nSdifLZJ1vaj7X5KZ6v5eflxs9lrbseapFfrPNVG8ba0SzrvMm6jxd7N0ZT7kC9uVgZjGHfvq4mj+wM2Wde32pl9vStWwkm+VpNUePeO+v0v/wq/Q/3JFuGPHacvZhQR4jdy2XsC2f1WYg9XN3upV78uQVh3DUiBvq+3EwAP308Sd+x5a0gYE0kKWmTTu4hovZnsdE7l9TohVNpH32PY3OWYvo7+/mumu+zK6d6wHo6+vmjtt+yqaNWfs4+JAnjHjvhb9+Oxf++u3cdeevKpbd27uHnp5dg4+S/r7uYcsHBsacNUwt7Lznz2PvfdvZtTPx5pdu4M7bslFYu3YO8IkPPshvfpbF37/2rSNjP09ddQ+nrrqHz3xs64jXPvb+rfzgW7to74AP/McyTj9n9oh1qnnW87OZzNbd388bX7SRm6/vob8/0d+fuPn6Ht74oo2DWRD+8sW137jVxGjFc9mtXM0t6Wq2pk30F2Y27E09PJDWcAUXMUA/7XRwwGBSRU0V25DGY/9ZRzKrbR79qZertv2MnX3ZdWrfQA+37PojG3rWAHDonFNHvPfnmz7Dzzd9htt3VU5g2TvQTc9A1+CjpD/1DFs+UOG2d3/qK1sna0cDDAxb3pd6x7sL1ASteBwq2pV2sI1s3KWp8VuP7Wf6cgiFJttLGJpLuBd4fErpojHeMxn/451PbaOfy9NEbyv7e2vh+bUppUZGek6IfL77LwJfjIilwFnAmQzNPV8afTkf+GRERErpk2XFbC08r+d7Kd9vWyutNI1sLTxv5n6YqHLL7SHrIP8ZWad4G/DZiJiRUvpUHdudalvJOjGhvtG+TZ1wLiIWA/+vsOjTwGtSSuXBPUWtdCevFY7LxeNvLZleRhURxwB/Vlj0jpTSB8d4Wyt9J7XYWnj+g5TSM6eqIiUtst+3jqP84rG0vJxW0gq/2elmIq7zmnmeLmZ7GE+5AtqjnePTGVzFb9nBVv7IL2hPHfQzNEf5ao5haVSev7GaiOC49Eiu5CL2sIvL+Q3tqYMB+kl5PO++HMy+cfCI9y6KZRyajuM2rmUX27mK3xIEQRsDDN1035eD2K/C+zW52ttncOxxf83Vf/ocO3c8wOWXfpz29k76+3vIYreDgw55AkuWjkzHOZYrLvsE3V1bRyy/8fpvDPv7+BNfweLFtoXpatasNj782eX8v+dv4Obre3nu49cyd36wZ1diYAAisoCDR55de9DAuvv7+OYXdwDZ+z/4ji188B3VZ+P52RX7Dfv7ic+cyw1X9/DNL+7gmiu6+eunrWNmHvLWk4e7RcCr3lJfvTQxWvFc1k8/a7mTe7kdgI6UZekopTaGbFTgcTySWTGZs1+qEtuQxqM9OnjEgidyxbYfsb1/E3/Y+h06YgZ9qY/SOLZD55zKspn7jV5QBRdv/V+6BnaOWH7Njl8P+/uUBU9jycx9hi27a/fV3LHnqhHv3dCzhg1b1gz+vU/nYRw7/5y666bmasXjUFFplHoHM1jOPqOuq8ln+5m+DDrQZHtS4fk3arhJDhMw93oFB1Hbzejyo836sr+L8zuvpEWllDYD38sfRMTewCuAtzM0+u6DEfHlsjTnxbmLD6ljk8V1e2jtzpxaFPfDARHRkVLqq7r2kPJ9Vj4X9ESVO0JKaXtEPAH4CVkASpAFm8xIKf17DdtsBXcz9Ds7so73HdXkejwWKOVF3Q28ZYyAA5ic41qtWuG4vLbwvCMiDk4pjWee9+JnWgP8cw3vaaXvpBateL5phf1efvw7BLikxvcWj6VjHkenUCv8ZqebZl3nTdR5urid/SOiPQ8WHYs9klXMj0U8Mj2BNdzMJtbSzR5m0MlCFnMAh7IkGjtszoo5nJYexxpuYQP308Uu2ulgPovYj0NYGdVvvB4Yh7E4LeNe7mQrm+hmN4lEJ7NZwBL25SCW1XnDRBNn3vy9OeW0v+GeNReyefPNdHdvZ8aMOcxfsB/7H/AoFi8xI4VGd9hRM/nGz/fmy/+1jd9f0MXGdX0sXNzGUcfP5K9etoBTz6wvqdJAYcBnXy9s2VR/oqA3v2cxZz1+Nt/7+k6u+1M3WzZmp5q992vn+JM7efYL53PcSa02C9PDV6udy1ZxOHOZzxY2soed9NDFAImZdDKPhSxlL/blIDqi9ilDNLFsQxqPBR1LOXPxX3Dn7qvZ2HM33QO7mRmdLOxYwYGzj2XpzJFzrEvlWu04VJJSGuw0Xsn+tEX7qOtrath+pieDDjTZirlGLhtr5YgI4IyJq86g08jSodeyXkkPcF3Z6xcXnq+MiINSSneNt3ITLZ9+4f0R8QBZamzIRhufBvymsOqVwFPy5yfnHdS15Kwqfod/qmdqiBZ1ZeH5LOBEamjPDN8P/cDVk1RuRSmlnRHxJOCHwLn54k9ExMyU0kdqKWOKXQqUcrk9upZOmog4lOZ3vhWPazemlHZVXXPIo2ose9hvJc9AMlZAQ71a4bhc3il8DjCeoIPiZ7qixn1W63fSKi4GXpc/PyEiZqeU9kxlhWiB/Z5S2hgR9zL0Oz+DbOqBUeUZgA4vLKqcC7I1tMJvFkZOixAV12oNzbrOm4zz/2zgOOBPNZR72tirPHx1xiwO5wQO54Sa3/O4ePaY63TEDFZzDKs5pu46LYglHJ2nclTr6+ycz6GHP51DeXrN7znnsaMn+Dn9zPPHW60RDjr4cRx08OPGXlGTbtmKdt7y3iW85b21v+eyNQdUXL7P/h1VX6vHKWfM4pQzWnYWKZVppXPZ3FjAXBawiiNqfo+mnm1I49HZNocj553BkXX8d/KJy1456uuPXvK8huuzeu7JrJ57csPv19RopeNQSURwFk+t+32afLaf6WfMeeOlJqs3XPVJwGSETv5VjesVr4wuTSmVz6V7ObC58PfLx1Wryfe/ZX+XD7cqjqZcCDxjrAIjYjnw5CplTFeXMXze+RfW+L6/Ljy/MqVUnk9sosqtKs9k8TTgF4XF/xoRf1drGVPoR4XnewHn1fCe/zf2KnWr67gWETMY/p2NpjyAYSJynU75cTmltJ7hnXCvHmeR9X4nSxk+LcB08CsYzEPZCbxgCutS0ir7vXieeXZE1HJn/QUMvy7+bXOr1FRT/pvNTcbxqVmadZ03Wef/MesbEQvB/+VKkiRJkiSpNRh0oMn2QOH52aOtGBFzgI9NbHUGnRURo964jYi/JBvRVvL58nXyFLvFOv9NRDyiOVVsTD7CsVbzyv4unyTyAuCOwt//VENnzj8D+WyRJIYyKUxbeWdBceLVV0bEqKHaEfFCoNgWPjNZ5Y4lHx39DODHhcUfjIh31VvWJPsl5JMBZj6aTxVSUUScxcQEHRSPa8dGxOIx1n8XtXcAri37+9Caa1W7Vjkuf7zw/JSIeP04yip+ptMjYqzMTh+jtTtMR0gpbQS+XFj0jxEx/uFv49Mq+714nllONnVQVXnww98XFv0+pXTjBNSrWVrlN7uV4R3lE3F8apZmXedN1Pl/B/A/hUX/r4bf83uZZsctSZIkSZIkPXQZdKDJdkHh+bMj4mmVVso7AH7E8FTHE+0rEXFKlfqcBXyusOh24FtVyvkEcFv+fA7wy7FudOfbWBQRr4mIX4y1bp0+HhEfjohR5/3NO4f+pbCoi7KU53mq7PcXFh0OfCciyoMViMw7gJcWFn8tpXR7+brT1L8w1NkyE/hRtX0cEY8HPl1YdBvwtUkud1T5aM4/B75XWPz+iPiHRsqbDHl7fENh0f7A7yLi8cVgm4iYGRGvIAuq6AA2NrkqF5IF1EA24vw/IkZO5pT/Jt4EvLPWglNKDwDrCov+plLZ49Qqx+WvMzzF+Mcj4k0RUfVaJSLmRcRbImJu2UvFz7Qv8I9V3t8REf9K7aOVW837gE358xXARRHxyLHeFBErI+L8iGjoeDGKltjvKaXfMnxqoHdGxEuq1GUZWbteXno72X5tZS3xm82ns7m6sOi1NWaVmCrNus6byPN/KXvJnLzcioF0EfFa4G+qlCNJkiRJkiRNurFGoEnN9hngfLIR9W3A9yPiK2Rzyq8HFgNnkXVULwW2k3UU1poWt1HfyLdxcV6fH5N1TK4kSz3/fKDU0dcHvCyl1FWpoJTSjoh4JvAHss+zlOzG8eXA94FrgQfJ5gJeChwNPJJsDvMZwN1N/mwLgRcBfxsRV5Clnb6abH/vBhaRzR38QuDIwvs+nlLaXuHz/XdEPB0oTY7zNOCGiPgs2RzY3cBh+TZPL7x1DTCekcstJaV0S0S8Ffj3fNEhwLUR8QXg12Tf8T5kqcOfw9Bc1z3AC0ZpPxNSbo2fqSci/oLs91D6ft8ZETNSSi053UJK6acR8SHgbfmiQ8iminggIm4n+00dRfY7gKxju5esfULWXsdbh3si4jtk3wdk6bmPjIjPADfldTiS7DdW6vD6FLVPIfBV4G/z5y8GnhIR1wHF9NwXpJQ+0eBHaInjckqpNyKeA1wKLMvr8lHgFRHxTbL5zR/M63ko8Ciy1OJzKRuRnFL6fURcBpyaLzo/Ik4jywxwJ9no4OOBl8DghJL1fCctIaV0X0Q8G/g5WcDLKuCSiLgA+AlZ+9tGto+WA8cCZ5LNL99Gk6e7abH9/mKyNrOE7LN+ISL+iuz4dhdZp+4ZwCsZCjgA+LeU0q8moD7N1BK/2dxXya5hAJ4ArI2Iq/NtloKxrk8p1RxsNUGaeZ03Uef/6yPin8gyGED2e70hP5f8juyYf3Be18fm63yd4VNCSJIkSZIkSVPCoANNqpTShoh4EdnosQ6ym+UvYqgDsGgX8FzgtEmo2qvIOspPIusMqTgiEugnu2E86lzPKaWbIuJUslHjR+eLT2Gow3GqnJw/xvI14N2jvP4Css6Ev8j/PgAYbUT8zcATU0pba9j2tJFS+o+I6AQ+TNapMJcssKJacMUO4LyU0mVTUW4tUkp9EfFc4CsMdVCdHxEzU0pvHm/5EyGldH5E7CCbtqA0lcc++aPo88DrgC8Vlm1rUjVeS5Y+u5Re/BHAJ6us+wWyEa21drS+n6yDqZSeewVDHU4lW2utaLlWOi6nlO6MiNMZPjr7SBobef584GKGOpTPyR8jNpuXfxHTLOgAIKV0UT5K+7vAfvnic/PHVGiJ/Z4HAz0a+BlD05k8Pn9U8x/AW5pdl2Zrpd8s2Sj+pwNPzP9exMjve9EEbbsezb7Om6jz//siYi+GfhOLyQJMzq+w+mfIgikMOpAkSZIkSdKUc3oFTbqU0neBxwHXV1mln2yk8okppZ9OUp12kI3+/DeyG/SVXAqcllKqlm63vMzbyeYGfjVZp/uoq5NlH3g/2b5ppk+TzW+9poZ1rwKek1J6QUqpt9pKeSr+vyTryLhplPI2A+8BTkop3VNzjaeRlNJHyDpzfg0MVFmti2yk71EppV9PZbk1brufLLCkOF/8myLi34vTFrSSlNI/kmXs+BhwI1kHzy7gVrLP8eiU0svzEaYrC29tylQLKaXNZKN9v0Z2DKvkTuDFKaWX1Vn2DrKsIa8CfgrcB+xpvLYVt9Eyx+X82HkCWXaH+8ZY/Rbg7xme9aFYzslkI/6ruQ54akqp1dPpjyqldDlZcMb5wFjH2j6yqXPOZwI6K1tpv6eUricbLf4RspH31VwJPCWl9PqUUrXjbUtpld9sSqkPeApZW/o/siwSuxjKctASJug6b6LO/68hOwdXO/7dD7wqpfSqWsqTJEmSJEmSJkNkU2JLky/vvDyRrHNiKVkn4Vrg9ymldaO9twnbPofCfM8ppeL873PJRogeAMwnS1V8cUrplnFuc3+yTskVZKP+uslS8N4OXJdS2jKe8musw95kHTCryEbPdZB11t0DXJVSamhqh4g4jCyd9gqykeYbyTp+L50uHTjNEBHLgbPJRtjPB7aQBXv8NqW0u9XKfTiKiA6y/Tc/X/T4ZqdSz39njwb2zxetA25KKV3RzO1MhKk8Lo9Sp2PIsjwsJ0vPv4NsGpo/1RrMFBGryH5De5N1uq8Frk4p3TgRdZ5qEXEo2Xe4jGxqkT1kQWC3kp1vdkxSPVbRIvs9ImaQdTofSrZf9pCd3/8wnYPiWvE32wom+zpvIs7TEdFG1maPJrtm2wjcBvxuoq+tHt/2F/4HUePSd+5JU10FTXMf+ny1hGFSbd5x0KljryRJE6h96ZKproKmuf7NE95VIEmj+uXAd+oegGrQgR6WRrsZLemhKyKeTzYHOWTza69IKTVrigVJUgvwOm98DDrQeBl0oPEy6EDjZdCBpKlm0IHGy6ADSVOtkaADp1eQJE1rtU75kI+6/mhh0f8YcCBJkiRJkiRJkjQ+Bh1Ikqa7f46Iz0bEY/MU6sNExLyIeDVwBdkUIJDNs/2ByaykJEmSJEmSJEnSQ1HHVFdAkqRxmgu8PH/0RMRtZHNfQzZ3+5FAe2H9BLwhpXTDpNZSkiRJkiRJkiTpIcigA0nSdDdQeD4TOHqUddcBr00p/d/EVkmSJEmSJEmSJOnhwaADSdJ097fAT4DHAScBB5NlOJgFbAc2AVcCvwS+llLqmqJ6SpIkSZIkSZIkPeQYdKCHpZTShUBMdT0kjV9KqQf4Wf6QJD3MeZ0nSZIkSZIkTa62qa6AJEmSJEmSJEmSJEmangw6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpAkSZIkSZIkSZIkSQ0x6ECSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpAkSZIkSZIkSZIkSQ0x6ECSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpAkSZIkSZIkSZIkSQ0x6ECSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDTHoQJIkSZIkSZIkSZIkNcSgA0mSJEmSJEmSJEmS1BCDDiRJkiRJkiRJkiRJUkMMOpAkSZIkSZIkSZIkSQ0x6ECSJEmSJEmSJEmSJDXEoANJkiRJkiRJkiRJktQQgw4kSZIkSZIkSZIkSVJDDDqQJEmSJEmSJEmSJEkNMehAkiRJkiRJkiRJkiQ1xKADSZIkSZIkSZIkSZLUEIMOJEmSJEmSJEmSJElSQww6kCRJkiRJkiRJkiRJDemY6gpIkiRJkqSHho4LrpzqKmiae8dBp051FTTN/f2dV091FTTN/dPBJ0x1FTTNDazae6qroOlu85aproEk1c1MB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJkiRJkiRJkiRJaohBB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJkiRJkiRJkiRJaohBB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJkiRJkiRJkiRJaohBB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJkiRJkiRJkiRJaohBB5IkSZIkSZIkSZIkqSEGHUiSJEmSJEmSJEmSpIYYdCBJkiRJkiRJkiRJkhpi0IEkSZIkSZIkSZIkSWqIQQeSJEmSJEmSJEmSJKkhBh1IkiRJkiRJkiRJkqSGGHQgSZIkSZIkSZIkSZIaYtCBJEmSJEmSJEmSJElqiEEHkiRJkiRJkiRJkiSpIQYdSJIkSZIkSZIkSZKkhhh0IEmSJEmSJEmSJEmSGmLQgSRJmjARsVdEvC8iLo6IzRHRGxEpf1w91fWbLI3sh4g4PiI+GRHXRcS2iBgovOfjk/sJJEmSJEmSJEmqrGOqKyBJkh6aIuIM4EfA4qmuy1RqZD9ExOuAjwPtE1QtSZIkSZIkSZKawqADSZLUdBHRCXyb4R3tNwIPAP3533dMdr0mWyP7ISKOA/6NoYxUPcA1wINAypfdNEFVlqSHhO7UxRpuZhNr6WbP/2fvvsPjusrEj39fS7Lce3fiOI4dpyekAAkEQgtlKQsLLOzChs7Sl96WXhd+hLp0lt57L6GFlkAKIb04ie3EjnuXbdXz++NeSVfSjDQzaqPk+3meeTRz594zZ+6cOTOa85730EgTs5jHClYzLxbXXG5Hamc9N7GNTRzmIA00MIPZHMExLI4jSh7TlbrYzO3sZTcH2EMrh2mnlUk0MI0ZzGcxR7Ka5phac7008ka6DXWlTnaznb3sZh+72Mdu2jgMwGncnwWxpKJytqY7uZNbOcBeOulkCtNYxHJWspbGaKq6Xho99dYP7WIrO9jCXnZxkP100UkTzcxiLstYyaJYXnOdNDp2be/gqx/fzaW/bWH7lk5mzJzEcac28y/PmsMZ95tWdXl7dnbyh18e4Mo/HeSW61rZvqWThgZYtKyR08+ZxhOfNZvlKycPWsbN1x7me1/Yy9V/O8SOrZ1EwIIljZxy7yn8yzPmsPqE5lqfrkZBPfVDAPvTHvayk33sZh+7aWEficRijuDkuG/N9dHoaW0/wO13/ZEde2+htW0fjQ1TmDV9OSsW34f5s1ZVXV5XVwe79q9nX8tm9h3czN6WTbS1HwDgXmv+nQWzVw9ybCebdvydfS2b2H9oC63tB2jvOMikaGTalHnMn3UMKxbdh+bJM2t+vhp59dYPdUspsZn1bOUODrCXdtqZTDPTmMFcFnEUx9IQzgMab/XWfvwcG1qklIbeS5IkqQoR8QTgu/nNLuChKaXfjWOVxkUt5yEiPgK8JL+5Cbh3Smnz6NVSkno9bNKTJvw/iPvTHq7kD7TTBkADjXTS0XP/ak5iZRxXdbmH00Gu4GIO0dJTbhedpDwe7AhWcVycPuC41nSYP/KTnttB0EAjHbT3bGukiVM4m3mxqOp6aeSNRhvan/bwV35d8r5Kgw5uSFewiduBrB1NoqGnXlOZzpmcZ/BKnai3fuj6dAWb87YDA9sPwCKWcxL3YVJM/JVY33jbVeNdhWG79YZWXvHvm9i3uwuA6TMncaili64uiIDnvHo+//aC6hLKPXTNOjp7X3KmTg862hPtWTNlcnPwmvct4iGPLT1g94Mv7+Wjb9tOV2fv/gBtrVn7m9QA//X2hTzm32ZXVa969K5Vp413FYat3vohgEvTRRxg74Dtd8fBmjjjxPGuwrDtP7iVK27+Iu0dhwBobGimo7ON7rkQq5c/hKOX3r/KMrdw6fWfKnnfUEEHre0H+MM/PtBzOwgaGprp6Dzcs62xoZlTj/lX5s06uqp61aN0xXXjXYVhq8d+CKA1HeIq/sx+9gCl/z+7P49iSlQf4KeRU4/t5570OQZwUde3o9pjzHQgSZJGw70L1/90Tww4yNVyHorHfM6AA0mqXGfq5B/8hXbamMkcTuQsZsRsOlI7t3E9G7mFdVzLzDSH+RXOLIdsJszVXMohWpjCNE7i3syJBXSmTu5gHeu4hju5jZlpDsuj76yvSUziSFYzl4XMZh6TmUJE9Mw8vpmrOch+ruFSzkmPoCkGn2Wq0TVabQjIZ+bMzS/zuJpLKj72znRrT8DBGk7mSFYzKRrYk3ZwLX/jEC1czaWcxYOqqpNGXj32Q4kumpnCMo5mEcuZwWwigtZ0iNu5kTu5lW1s4lauZQ2njPQpUZVaD3fxxufexb7dXaw5sZnXX7iIo49tpmV/F1/6yC6+9dk9fPb9O1lzYjNnPaDyAZHODjjl3lN41JNncdYDpjFvYSOdnYnr/36Yj7xlO+uub+M9r9zKyjWTOeb4vhkL1t/S1hNwcOb9p/KiNy/kqNVZdpXbb2rjo2/bwVWXHuIjb93O6febxvKjzLwynuqxH4LsO9EM5vR8Fm5nEzvZOpJPXSOks6udq9Z9nfaOQ8yctoSTjn48M6YuoqOzlds2X8yGrZewbtNvmDVtKfNnH1NV2Y0NU5g1bSmzpi9j1vTlXH3rtyo6blI0smLRfZg78yhmTT+C5qYZ2Xfqrk527b+Nm+/4FS2Hd3D1bd/mfie9lKbGKbU8dY2Qeu2HOlI7V3AxBznAdGaxhpOZx2ImxSQ6Uyct7GUrm5jkaqfjql7bj59jQ5v44cuSJKkezS9cv2PcajH+ajkPnjtJqtEmbsvTIzZyKvdjRmSzLRujiWPjVBayDIB1XFtVudvZzD52AXAq5zAnFgDQEA2sjLUcSTYr61aupyt19Tm2KSazNk5jUSynOaYSkU0WmBSTWBBLOY37AdBOGzu4q8ZnrpEyWm1oBrN5II/l9HgAq+PkqlLZd6VObuN6AFawhqNiLZPydK9zYgGncDYAe9nJdmMVx1099kNHcAz345EcEycyM+b09EPNMZXj4l4s5SgA7uBWOlMnGl8//to+tm7qYOr04F2fXcrRx2YBANNnTuIFb1zA/c+fTkrwmffvrKrcD31jOR/+5hE8/F9mMW9hNg+toSE4+cypvP9Ly5k7v4HODvjO/+0ZcOzvfrKfrs6sDm//5FJWrplMRBARrDqumXd+einTZgQd7XDJb1qGfQ40PPXYDwGcxYO5bzyUE+IMjohVTMZB4Xp15/YrONy2l4ZJkzlt9VOZMTXLxtXY0MyxR57PwjnZ7OJbNv2mqnJnTF3Meae9hjPW/gdrjngoi+ceX/GxTY1TWLviESyaezxTJs/s/U49qYEFs9dw2uqnAtDecYjte2+qql4aefXaD63j2jzgYCZn8SAWxNKeLE8N0cCsmMeaOJnJ4XJB46le24+fY0Mz6ECSJI2G4tSWjrJ73f3Vch48d5JUoy1sBGAJRzKlRJr5ozgWgP3soSXtr7rceSxmZswpUe5aANo4zC62VVXnaTGDxrzrb+VQVcdq5I1WG+oenKvFLrbRRisAK/LHL5oVc5lHNhjQXX+Nn3rsh2bHvJ5AlVKWsRKALjppYV/FddLo+PUPs3bxkMfOZOGSgUlq//V5cwC45dpWNt7aVnG5p96n/PIrc+Y3cJ8HZVkTbr6mdcD9u3dkwSjLVzYxdfrAn5Onz5zE8pVZpp7DBwf+SK+xVY/9EFDz56DG3pad1wCwZN5JTJk8a8D9K5ecA8D+g3fRcnhHxeUO5/vQUKZNmUdjQzYA2NpWebvW6KjHfqgttRYyh51CY5iVp17VY/sBP8cqYdCBJGlERMTkiDg/It4TERdFxIaIaImItojYGhGXRcSHIuKsMapPc0Q8PSK+HBE3RsTOiGiPiD0R8Y+I+GJEPC0iZpQ5/vcRkfLLWyt8zLcWjvn9IPsNKDsy/xQR346IdRFxMCJ2RMTfIuINEXno5SiKiPtFxEci4ur8sVsj4q6IuCQi3hYR5RfX6/e8gAsKd11QeL7dl/UjWO+GiHhCRHwmIq6NiO35a70vIq6PiG9ExPMiYv7QpY3PeShug3yqWebzJY75whCP/4D8vfb3/L3XFhHb8vfgeyNqWPAsK/de+fv7bxGxOT8vO/Pz9OGxeG9HxKqIeEtE/LlQhx0RcU1EfCwiHlBhOSv7ndOV+fapEfHMiPh1RNyRl78tf01fFhGjEsIcEdMi4nERcWH+WJsi4lBEHM7b3p/z127taDx+oR5HR9aP/T4ituSP391v3hARP46IN0fEmUOUMykizs1fq59GxK0RsT8va3tkffAnI+IhVdRtfeH1ekaFx3yh0vdNv+POjoj39WvrLXkdfhYRr42IgaN+pctqjIh/jYgvRfZZtCsvb1NE/CYiXh0R8yosq9hmz8u3Tcvb7M8i4rbIPjsq/ty6u+lI7exjNwDzKZ1icTbzewb4qwkO2M32vNzFJe+fElOZzqx83+qCDlrSvp71Q6cyvapjNbJGsw0NR/fjzGB2yR/doLe+u/K2qvExUfuhJnqXdelez1bj4+CBrp5B/3JLJ5xwrylMn5n9pHvlX0YuWG3WnCwwpbNEzMCSI7I2u2l9O4daBu7Qsr+LTeuzAIg1Jzk7dDxN1H5I9aOjs5V9B7PMSfNnl/75Y/b0I2hsyN7ru/bdPmZ1G0zLoR10dB4GYGrz3HGuzT1bvfZDW7mTRBdNTC5bL42/em0/qszAcFlJkqoUEY8GvgSU+1a/KL+cCbwsIr4PPDOltHeU6vN04D1Aqby1s4FT8st/AAciYmlK6cBo1KUSETGX7Pw9ut9dU8lS7Z8FvDwinp1S+tEoPP4i4LPAY0rcvSS/3Bd4fUR8FHhtSqkuZuBHxCOBD0GJaX8wEzg+v/wr8JGIODWlVDLP3kQ+DwCRBUN8Eig1kLswv5wJvCoi/hd4ZSX1z8/L/wJPLHH3vPxyMvDSiPg68NyU0ojmVI2IRuDdwMuA/oudz88vJwEvioifAc9KKVW1qFpEnAJ8g6y9FC0EHphfXhwRD08p3Vb9syj7uM8DPgiUW5C3u+2dA7w6Ij4LvDSlNHAK2vDq8Sbgvxl4fiHrN2cDx5H1U2+LiH9KKf2sRDlnAj/O61zKgvxyCvD8iPgj8JSUxj8feB5I8HFKv4cmkwUEHQU8EnhvRDwzpfSFQcp7OPBRYE2Ju5fllwcDb4iIV6aU/q/K+p4OfJ3S/d89Ugu9Mxy6fyjoLyKYlmawj90Vz+ZtS4dpJxtImVGm3O77WthXUbkpJdo4zG52cGueEnIK01iQp4nU+BitNjRS9SpXp+y+mQC000pbajUl7DiZSP1QUfcPsEH0tCWNjw3r2kh53MfKNaW+lsGkScGRq5q48R+tbLil8kwHQ/nH37IAhqOPHfi4D/vnmXzpI7to2d/Fm//zLl705oUctTr7sX/9zW189G07OHggcea5U7nPeQbQjaeJ2g+pfrQc6s1cMGPqwpL7RATTpixgX8smWg6NX8BjSom29gPsPrCBdZt+C8CUybNZOMd/kcZTvfZDe9mZ3z+bRBe3p5vYwh0cpoUGGpnFXI7gGBaG/5ONp3ptP6qMQQeSpJGwkr4BB/uAdcBeoAFYCqwGunMQPR5YFRFnp5RGNI9wRHwQ+K9+mw8BNwK7gRl5Xbpnls5gfD8PG4AfAufmt3cBN5BlIzqO3vO6APhuRDwppfSDkXrwiFgB/AYohq93AtfldVlO74BZE/AK4PiIeHyJQc+/AYfz6ydDz8jJZuCafvtWNSBcpu6vAN5P38xN7WSv9XayQdyV9A5+NpMFcpQqa7zPwy8L2x4IPYuCXQts6ndM/zKIiLPJBnqL2RwOAdeTvQ/nkQ3KN5K1uZcCayLisYMFHuQz638Bec7dTHte7k5gVv4cu0c2ngqsjYjzUqoiv9kgImIy8D3gn/rddStwBzCH3ucG8CjgLxHxkJTS+gofZi1ZwMGc/PYtZOd9GnAqvc9vNfCriDglpXSw2udSxrH0DTjYCdxO1o82ASvozX4xCXgesCIiHpVSGpGpiBHx38Db+22+A9hA1pZn5HVYWri/XMa07iCJbgfJzuceoAtYTHa+u3M8nwv8NSJOTymN269VeeaA79PbBrqtI3vvBtl7eRW9n2X99y2W9wKygINiLuudZOfiEHAEvX3KHOBzeQDcuyqs8irgA4U6bATWk/Ud99hf2Fp7ul5oHmRtxWamArv77F95ueVTU3ev5zhYudeny9nM+gHbZzCHU7gPDYOkP9foG602NFzdjzN0nTJtHGYyBh2Mh4nQD/XXkTpYTxaTu4jlphoeZzu39X41X7C4/L+p2X2t7Nw+MjHQf/rVAW66Ovu35pFPGhh4snBpI2//5BLe8dKtXP6nQzzz/I1Mbs6+ErW1JuYuaOBpL5rLf7y0ogROGkUTsR9SfWlt7/1XvrmpfCBa932t7WM/h+e69T9i846/D9g+c+oSTj7miTRM8rNsPNVrP3SQrK020MjlXMw+dhEEDTTSThs72cpOtnJkWs3aOK2iOmnk1Wv7UWUMOpAkjZS/A18EfppSWtf/zohYQjbQ+Wqyz59TgXeRDd6OiIh4FX0DDjYAbwC+l1I63G/fE8hmv79gpB6/Rs8nm0m9m2wW99e7B4Ejogl4Otks6Flk5+1LEXFCSunO4T5wRDSQzZItDrR/Fvjv4izx/Fx9nGwwHLJZvu8CXlUsL6X0msIxX6B3aYGLUkrPGG59+9X9iWQDbt12AG8BvpJS2tdv36PJZum/sExZ434eUkqPKByznt5B5g8MNpM63/8I4Ef0BhzcAbwW+G5Kqa2w31zgdWTvwcjr/1ay2e2lyp1JFsiwMt+0K9/3S8VMBhExDXgR8E6y2eCnA58AnjZYvavwDvoGHPwZeGFK6epCHRYCb6P3/bwK+HpEnFthNoqvkA3efhd4XbEPi4jZwIXAs/JNxwAvJ3vtR0ICLga+Bvw8pXRH/x0iYhXZa/q8fNMjgJcAHxnug+eZLN5U2PQzsiwYN5bZ95HAs/N6l3ML8HngJ8B1KaU+OXjztvgcsvfsdLIB+E8BT6j9mdQuIo4BfkCWzQGgg+zcXphS2tRv37nAY+l9LUqV90iy7CDdwQl/Ivss+lMxUCQi1pC1re4sN++IiMtSSr+qoNofIsvm8kfgZSmlnl/c8kCde2SuyC563+6TKD9435Df10llAzWdFZfbOGS5jTQxmWa66OpZUmEGcziO05gWzi4eb6PVhoaru16V1AmgY4zqpYEmQj/U341cSSuHaKCR1Zxc8XEaHYcP9X7Fap5Sft3g5qnZfaWWOqjW9i0dXPiGLPbznIdO594PLJ2p4N4PnM7/+/Iy3v7SrWze0E5ba29d21oTB/Z10Xq4i6bJBtCNp4nYD6m+dHa191yfNMjgfffAfmfXyGVcqVRjQzOTG6fTlTp7llSYOXUJa1c8kulTKlpZU6OoXvuh7v+/dnAXAEdzPEdxLI3RRFs6zC1cy12s5w7WMSvNZWkchcZevbYfVcagA0nSSPhCSuljg+2QUtpClkL6arIBXoDnRcTbU0p7hluBPC32ewqbrgQenlLaUWr/lNL1wFsi4r3A2P+H1Gsh2azX81NKlxfvSCm1A/8XETcDvyWb9TyTbHb/U0fgsZ9NlrK923tTSq/vv1NK6fqIOJ9sMLI77fgrIuKLKaUBs+5HWz7o97nCpvXAQ8qlvE8p3Q68P8+CUeq7z4Q8DwWfIcuEAVkWhPNSSrv675RS2g28NiJuJRvgBXhNRHyi/8Bq7n30zsS+Ezi3VOaAfMb/+yPiGuCnZDPg/z0iPpxSumwYz4uIOI6+QR2/Bx7RP7tEPkP+hRGxg94B9PuSBfX8bwUPtQD4REppQGBKvgzMs/NsGA/NNz+LkQs6eNtQy7vkbfv5EXE7vf3cKyPif1NKncN8/PPpXVLhduDxxWCVfvXYRhZc9sU8WKeU3wJrB8vCkLfF90fEJWSvaQPwzxFxbErp5tqexrB8nt6AgzbgcSmlX5TaMa979zmY0f/+fNsX6Q04+BLZch8DXqeU0i0R8Vjgy8C/58dcSJa5YygzgYuAR/d/vfLbGwc7OCKuKHffQ6PUSioaKcfGqRzLqUC2VuUOtrCOa7ic37MireHYOHWcayjpnmR9upEt+UfGCZzB1DAt/j3NoZYu3vT8u9i9s5PFyxt5zf8sKrvv5z+4ky99ZDdHrZnMuz+3lBNOy2YCXn/VYT713p384Mt7+fulh/jot5czc7aBB5JGz9ojH87aIx8OQEdnKzv23sItd/6ay2/6PEctPptjjzx/nGuo+tT7M8USVnBMnNhze3JM4UTOpCXtZR+7Wc+NLMWgA6la5dKiSpJUsaEGzPrt+w3gL/nN6cDDR6gar6V3QLkFeGK5gIN+9Tk0AoN2w/W+/gEHRSmlP9F3RvO/RMTiEXjclxauX0Pf2c7969AGPJMsVTpkg2MvLbf/KHsx9Cy+1QU8tVzAQVFKqaN/xovcRD0PRMRpZLPeIVv24MmlAg6KUkqfJhsYhiyQ5fklyl1C9jy7PWOopQryQdovFDa9ZLD9K/QSer+vHsrr0X85i6K3AlcVbr80IspPEeu1noHLsvT3/sL1VREjs8hfNf0nWSBId5aTFcCZI1CFIwvX/1Yu4KC/cv1mSulgpcs+5H3bN/ObQbb0zpiKiHPpXd4G4E3lAg76K/PaPYcsmAyyJUCeN9hnTH6uXki2/ATAiRHx4Aoevh14dqWv1z3FpEJcWRflP9o78/saKozBb6i43I6qym2MJpbEkZzJg2igkY3cwraSMWAaK6PVhoaru16V1Amg0fkl42Yi9UN3pttYx7UArOEUFseRQxyhsTBlau9X19bD5b9SteYZEaZOr/2n3bbWLt74vLu46epW5sxv4H1fXMbseaWDBS76wX6+9JHdzJ3fwIe/uZyzHzyd2fMamD2vgbMfPJ0Pf3M5c+c3sOGWNr72id0110nDN5H6IdWn4tIEXYWsB/11Z0RomDS57D5jobGhmSXzTuKs455FY0MzG7ZewrbdN4xrne7p6rUfKt5e0SfZKYXt2dybFvbTOrIrAqtC9dp+VBmDDiRJ4+GSwvV7D7ewiGgEnlzY9IV8dvtE0EmWsn8oH6U3JLeJLL13zfLMECcWNn14qDT0edr3bxY2/fNw6jAM/164/vOU0qW1FjTBzwPAMwrXf1IqJX4ZXyxcf2iJ+58CPYtB/z2l9Jsayn1I2b0qVxyE/m5KacNgO+dp/D9Y2NT/9S3nMxUM3v6JLMilWyXljqj8+f21sGnY/SdZMEe3UwbJYDBaRvTzoAbF/mQHw1+y4hmF6x8ZIkgGgHxJmO8XNpV6T/b3s1JLcVQqpXRGuUutZdaD4nqPg62/2Jo3+8HWh+xbbu96j62U/+GpLX/MSsvtNiWmsojlAGxmfVXHamSNVhsaruYK1hUtts3JY1QvDTRR+qG70gZu5EoAVnECR8WxFdVDo2/B4t4fuHdsLf9vSfd98xfW9oN4e1viLS/cwt//cogZsybx/i8tY8Ux5QcOv/f5PQCc/4SZzJ478Ovi7LkNPOzx2TJBf76oZcD9GjsTpR9S/Wqe3LvkV2v7/rL7dd/X3DQgAdy4mDJ5FgvnHAfAph1XjW9l7uHqtR8q3p5G6aXtitsPD/IYGj312n5UGUM1JEkjKl9b/WHAqcAyshnpzf12K4aTHjECD3sGUPwv51sjUOZYuTJPWT6olNKGiLiO3rTb9yFLq1+rs/vd/nGFx/2Q3hnwCyJiTUrplmHUoyp5hoe1hU3Dfa0n5HkoeGDh+kVVHPePwvUzIiL6zU4fiXKXRcSylNLmKo7vERFHAUsLmyp9bX7U7/bZkE/jK+/PQxWaUjoYEbvoXcpiToX1qVi+hMODgVOAxWQp9Pv/+ltcbHkk+s/iEhjHA1+JiNcMZ0C7W0TMIvs8OA04iuz5TKF36QEgH2nNjMTzqVaxrf+4TDaUikTEHPq+PrW+dyrJYPGHKsq+x5he+IGohX19bndLKXGQA/n+swbcX8rkaKYpTaadNg6wj/ksKbnfAfZVVW5R9w8gh6gm+YlG2mi1oeGawSx2soWWvI2V0kL2w38TzUyO/l+9NVYmQj+0Nd3J9WRJ1lawhlVxQkV10NhYccxkIiAlWH9LW8lAgK6uxB23ZTOMj1pT/Qzjzo7EO162hUt/e5Cp04P3fn4pq08YvN/YcGsWn7vkyPLruy9dkd235U7XQB5PE6EfUn2bPmVBz/UDh7b3ud0tpcTBw1ly0elTFw64f7xMacra+6HWQRNAapTVaz80ndnsYEtFj6XxU6/tR5Ux6ECSNCLyAcL3k81MrubzZc4IPPzx/W6XXaqgDl1T5b7dQQfDnY5UDPzYUkngQ+7qEuWM5WD7SL/WE/U8kC8bUFz7/TkR8bgKD59auD6ZLDhob2HbKYXrj4moeZHxhUBNQQcwINfdP0ru1U9KaU9EbCRbfqBUOaVU+l9nC71BB9MqPGZIEXEScCHZDPdKloPoNme4j51S+nNE/AU4J9/0FODJEXEp2TIcfwEuSSntqbTMiJgPvBv4D6gqNHxOFfsOW0RMom9fOtz+5GT6ZpL7SERUunxPMfiikl/thlxS5p6oMZqYleayj93sZGtP9oCiveyig2ygZh7l163uby6L2Mad7GIrR5X4CD6cDvUMCFdTbrdDZLNCTeE4vkazDQ3HXBaygZs5wF5a0yGaY+qAfXaydUzrpNLqvR/anjZzLX8lkVjOKo6t+SueRsu0GZNYe3IzN17dyhV/OsgDHjFwBvENVx2mZX+WgOv0cwb2B4Pp6kq851Vb+eMvWmieErzrM0s58fShy5g0KYDEts3lU61v3dSeP4dqvs5qpNV7P6T619jQzKxpy9h3cDO79t3K4rn9f4aBvS130tGZJXWbN+vosa5iWYfa9gDjv+TDPV299kPzWMQGbgLgIPuZxbwBxx+kN7vH1JH72UdVqNf2o8r4i4Ykadgi4izgV9Q2YDQSU7GK3xJbUkoHR6DMsbKzxn3nDvNxi8dvr+K4/vsOtx7V6v8fQaVBAuVM1PMAMJu+3+VOH2ZZxaCD+YXrxzMw2KOacmvV/5xW+/p0Bx1U8toMmQK/hBH5NTUi/gn4LrX1hSM1lfWJZJkkulPrTyILQugOROiKiCvJMot8LqVUdtpIRKwCfkfv+a/GWE/NnUPfIIHh9ifz+92uZJmEUip535Sf7nwPt4QV7GM3W9jIqnT8gMHZDdwMwEzmMj1Kp9QsXe6RbONOdrKV/WkPM2NOn/s35uVOZgpz+/040ZW6mBTlVzY8mPazPY/PmsPAmWQaW6PVhoZjHouYTDNttLKBmzmWvgPF+9MeduVBB0s4ckzqpPLqsR8C2Jm2cg2Xkkgs5SiO415VPjONlYc8biY3Xt3Kr3+4n/946TzmL+r78+03P7MHgGNPbh50SYT+Ukp84PXb+c0PD9A0Gd7+ySXc6+zKBlSOOW4yV192mN/+6ABPf/E8pk7v+7l2qKWL3/04m3F4/GmmIx5v9doPaeJYMv9k9h3czF07r2HV0gf2WXIBYMOWbJW8mdOWlsyEMBqG+k7dcngn2/ZkK07OmVnLv6MaSfXYD81lIc1MpZVDbGQdJ5VY4XFjPp9oFnOZHH6ejZd6bD+qTPleWpKkCkTEdOB79AYctANfIZstezLZIPGUlFJ0X4C3jXA1it8Ca06NPU6GWke+qDgwOtzBueLxtdYBqpvFPBL6P95wX++Jeh4Apo9gWf2/E45U2cP5rtm/jdf6+tTtf4kRsRz4Jr3P9SDwSbKMMceT9avN/frPL450PVJKd5Et2fIs4FIg9dtlElnK//cB6yPiP0uVk2cO+Ba9AQeJbCmSZ5AtsbAAmNrv+TyzRFFjZaT7k7F833SN0GPd7SxnFVOYRicdXMWfOZCy+IyO1M4t6Wq2swmA1Zw44Nhfp+/w6/Qdbk3XDbhvIct6ZsJczSXsTVkcYFfqZEO6uefHqWM4YcCPoTdzFTelq9iTdtCZepNftKc2Nqf1XM7FdNFJA42sYM0InAUNx2i1Iche87bU2nPp1kl7n+1dqe9bfFI0sIosBf5GbmFDupmuvC3tSTu5muyH/9nMZ2EsG+YZ0HDVYz+0J+3gH/yFLrpYzJGcwJlkSbNUjx7zb7NYvLyRgwcSr3/2Xay/JfsafPBAF598zw7++IssO85zXtU/3hEedPQ6HnT0Or7woYGx7f/7jh387Fv7aGiEN390Cfd+YOVfXR77tCwmcuvmDl7zjM3cfO1hOjsTnZ2Jm689zGuesZmtm7NlFZ5wwZxqn7JGWD32QwCdqaPv513+lbaLrj7bO5JLdIy3IxaewZTJs+nsauPv677OgUPZHICOzlZuvuMitu25AYA1yx8y4NiLLn8bF13+Nm7d9PuSZbd3HKKt/WDPpVtHZ2uf7V1dfZPG3bTx59y48efsOXAHnV0dhfIOs3nHVVx+0xfo6uqgYdJkjlrcfyVNjbV67IcmxSTW5CsibmEjt6br6EjZbPm2dJjr0+XsYzdAz3dvjY96bD/g51glzHQgSRquZ9K7Dnc78LCU0sVDHDPS08J2F64PZ2b1cDXUcEw156K4mNTesntVZs8I1KF/OWNhd7/bs2FYC2DvKVyfSOeh1GM+IaX0/REsu3u6witSSh8coXKrrUPRTCp/rYuvT/9y6snL6R2o3guck1K6fohjRmVabUqpE/g88Pl8eYRzgfsB55FlQOgemZgJfCIiIqX0iX7FPIrebAkAT0spfW2Ihx7NacJD9cml+pPh2NPv9ryUUv/H0ChriAZOTedwJX9gP3u4lF/RkBrppPef/tWcxPwovX5jORHBKem+XMHFHKKFy/gdDamRLjpJeZzOclaxPFYNOLaTTu7iNu5gHQCNKVvzujsdJGQzKU7hvkwJU3iOt9FqQwB/5dccZmBCrmv4a5/bp/OAAek8j4hj2J/2sInbuYWrWcc1TEoNPfWaynRO4b5V10kjrx77oVu5ji6ywZtdbOWP/GRgiGHuWE5jSZgxYzw1T5nEOz+9lFc+bRO3XNvKM8/fyPSZkzjU0kVXF0TAc149n7MeUPlnxtZN7Xz389m/kBFw4Ru3c+EbyycS+95lfdOlP+SxM7nhqsN89/N7ufbywzz/MXfSNDn7etjelnrKfdYr5lVVL42OeuyHANZzE7dzw4Dt29nck/UJYClHcSJnVVU3jayGSU2ctvopXHHzl9h/8C4uue7jNDY009HZRvcHyOrlD2H+7GOqLvvS6z/F4baBP2ldc9t3+tw+49gLmDdrZc/tzq527tr5D+7Y9jcgaGzIYvc7Ontjxyc3zeCUVU9iymTXYh9v9doPLYkVHEh7e/qj9dxIY2qivTDPZQ2nsCCW1vCsNVLqtf34OTY0gw4kScP1iML1r1cQcACMeN7XuwrXGyNiVUppuOtdF2dVN1V4TC0p9qtZ/K74jWdrDY9VVEwjviIiGlOqKAyz/3+Uw01HXq27+t1eC3l4a20m6nkgpdQSEQeA7oVeF49g8VvoDToYyXKr0f+cHsPA13+AfLZ98X015q9NFYr954crCDiAke8/B0gp7QR+kF+IiKXAc4HX05sd4D0R8cV+y9kUn88fKgg4gMqfz4j3ySmlQxGxl95gg7UVllvOln63FzMwsEFjYGbM4b7pfNZzIzu4i1YO0UQzs5nLCtYwL2rr1qbENO6THsp6bmIbmzhMCw00MpM5HMExLI4jSh63krVMZya72M4hDtDGYbpITKaZGcxmPktYztE0RqVNW6NttNrQcB0fZzAvLeJObmM/e+iik2nMZBHLWcla21Adqbd+KBUiDNqHSB7VHZyg8bX6hGY+/8sVfPXju7n0ty1s39LJrLkNHHdqM0981hzOuF91A/upEGTS0Q67d1T/Or/4zQs556HT+cnX93H9lYfZlZex5IhGTjpjCv/8H7M58fSpQ5SisVJv/ZAmnpnTlnD2iS/k9rv+yI69t9Dato+mxqnMnr6cFYvvy/xZpQflRsvRS+7P9CkL2L1/PQdbd9HWfoCu1MXkxunMmLqIBbPXsGzBvWhqrNtkh/c49doPrY6TmZsWcge3so9dtNOWp9NfwArWMDsGZhLS2KvX9qPBGXQgSRquowrX/zbUzpHl8TxnqP2qdEm/2+cBww06KK6XPa/CY06u4XHOiohJKaVBU2VHRCNwemHTFTU8VlHx+Cl52UO+fvR97TqBq4ZZj2pdC+ynd3b0ecBvh1HeRD0P3f4CnJ9fP5ssNf9IlXtSodzxcA1Z9pTuEZRzgD9VcNwp9E1zf/kI12skVdt/zoB+C3mPgXz5hbdHxGbgM/nm2WRLMvyusGtVzyd3/wr3G60++RJ6gyXOq7Dccq4GWuhtf2cDNw6zTNWoOaawltNYy2kVH/PQeOKQ+zRGE6s5idU9XeTQpscspjOLlRxX8TEaf6PRhu4fjxpmrWBxHMni0Y8/0wiop37ozDiv4n1VP+YtbOQlb1nIS96ysOJjfnf76pLblxzRVPa+apx+zjROP8dMBhNFPfVDAMfEiRxTIhW26ldz0wyOW/FI4JEVH/OwM98y6P3nnvJfNdVl+tQFHD31/hy9tNJ/IVUP6q0f6jY/ljCf6jOXaWzVW/vxc2xow1lnV5IkqHzGabdHAMtHsgIppa30HfQtud54lTYUrp8y1M75TOBactouobKBrkfRd9ZuJRklBvM3+q5f/vQKj/uPwvUrUkrDWdqgankWgt8UNj0rIpqHUeSEPA8FPy9cf3xEVDoYW02594+I4c4Ar1pK6TD0yTf9tKhs8eELCtfbgEtHtGIjq9r+8+nA5NGoSIW+2+92///Qq3o+EXEClQe1VNsnnwGsqKDcXxSunxsRtf1iAaSU2oFfFzY9p9ayJEmSJEmSpInEoANJ0nBtLlx/wGA7RsQ0YLTWhv9Q4fpZEfGSYZZXnB1934gYavDqHdQ+GPjuiCi79nhENAHvLGy6jb6zi6uWD5J/vbDpeREx6BTMiHg6cK/Cpk8Ppw7D8KHC9eXAu2staIKfB4DPAbvy6zOB/x2hcn8E3JRfnwR8Jm+HY+0zhesnA88YbOeIWAO8oLDpWymlPSNfrRFTTf+5GHj7SFegwkCObjP63d7V73Y1z2cS1bXXYp/8qDzrQ7myA3hPheV+HiguKDrctv7+wvVzImIkguAkSZIkSZKkumbQgSRpuIqp7Z8YEY8utVNEzAd+wvDXzC7na/RNlf+hiHh5PrBVUkTMiIhXRsT0Enf/DDiUX58EfKrUQFRk3gQ8exh1vw/ZQNeAoIWImAJ8mb5pwt+dUnFVzpr9D72z/CcDP4mIkovyRcTDgE8VNt0CfHUE6lC1lNLFwI8Lm14REe8bLONBREyOiGdHxMoSd0/I8wCQUtoPvKGw6SkR8fWImDPUsRFxVkR8KSL+rUS5XcDLoWcB4HOBX0TEsgrKPSEiPhYRr67oSQzum/RNT//xiHhEqR3z1/ZnQHc7aKXygefxUuw/XxQRZ5baKQ96ughYMAp1+FBEvL9cmy/UoZHsvdLtMAOXtik+n3tHxAsoIQ9A+wrVLWdQzLIwh77BR8Wym8iWGXlYJYWmlPbRN5jjvsBPI2LRYMdFxMMi4qElyvsz8I3Cpo9FxGvz8zdYeU0R8diI+F1EHDXYvpIkSZIkSVK9GfTHL0mSKvBp4LVkM2AnAT+MiC+TDQpvJVsS4FzgWcB8snW5fwo8dSQrkVJqj4gnk6VjX5DX5ULguRHxDeDvwO68nmvI1hH/J7K1tz9Xory9EfFx4JX5pkcAV+TbbiAbnD6JLM3+acB+4FfAv1RZ9R/kZT+TLKPCZ8jWsg+yFOLPz+vb7VcppQH1rUVK6aZ8YPij+aZjgKsj4v/IljDYDSwD/hl4cl4nyFLWPy1Pfz9eLgAuI6szwKuBf42Ir5G1gZ3AVOBosvTtjyVri/fqX9AEPw+klD4VEfciaysATwEeHRHfBP4AbCKr62yydPP3IhuQ7R7Y/C0lpJR+HhFvoHfg/sHAbRHxvfyYjcBBYBZZxonT8n26M0W8bQSeW2tEPA34M1kwwRTgZ3kdvgfcSTYA/SCyVPbF2e+vTSldP9w6jLIPkWVvaCDri/4YEZ8lCzDYBSwCHpLvMw24g6x/GP6i4L1mk72fXhURl5Mt3XIVWf99kOz8nkK2tMPxxbrnA/ZF3ybLPNK92PjHI+J84Ftkr9VM4N5knwdHAu3Al6ggaCuldHNEfJfePvbZ+bIfnyPL/jINOD0v+xiyrAvXUUHwQUrpwog4p1D2w4BbI+LrZFll7iJ73y8DzgQeR9a3vJy+yyl0ezZwbF6fBuC9wAvz9+TfgO15eXPy/c4Ezid7LaC3j5EkSZIkSZImBIMOJEnDklLaFhEXkM1IbiQb7L+Avuuqd2shGxC9zyjV5baIOJu+GRWOp/bBzzeTBSd01/dk4BMl9msB/jXfr9qgg3+QDZ7+H1ldLxxk30uAJ1ZZ/qBSSh/LMwS8n2ygazrwkvxSyn7g8Smlv41kPaqVUtqdDxL+gN414VcAr6uxvAl5HgpeQDYg/Xay9+AMsoHP4WTgIKX03ojYCnycbMC/mSxgaESDhoaowxV5doMfkA3KBtn7rNx7LQGvSyl9eGxqWLuU0rUR8Qqgu65TgBfnl/62A4+nfJscCWfml6F8lax/7CMPEnkyWbDOtHzzP+eX/trJ2m0nlbfTFwOnAqvz2/fPL/1tJws0quZcPQX4GL3BOzOA5+aXqqSUDkbEA4Ev0NtOV5AFR0mSJEmSJEl3Oy6vIEkatpTS94CHAteW2aWTLAvA6Smln49yXdaRzbh+FdnM2sHcBLwROFCmrINkM14/CXSU2gX4PXDmcJ5XSunLZDO1ryqzywHgHcCD8nT6Iyql9AGygInfAF1ldjsMfBE4IaX0m5GuQy1SStvIsmg8B7h5iN03ks02vnWQ8ibkeQBImXeRBcZ8lWyG+mB2A98hGxD92hBlf54siOd/gb1DlHuALJPJBfRd235YUkq/B04kC84pl1kikc1KPzul9L6ReuzRllL6CFkw0foyu7SRZRA4JaV0RZl9huNTwGcGefyiK4Enp5SellJqL7VDSulSsiUK/jJIOZcA9682a0tKaQvZe/5b9C79UdRJFpxyWrXnKqXUkVL6T7JsHRdTvg+A7H3wBbK2Xq68AymlJwKPJPuc6ByiCuvJgtrun1JaX2m9JUmSJEmSpHoQI7MktCRJEBFBlk76TLKlFPaTpaX+Uz5YNB51OoksnfxCsnT7+4ENwN9TShurKGce2WDUCrKMDpuAS1JKt9VQp98DD8xvvi2l9NbCfSeTBU0sAw6RDZL/NqV0qNrHqUVELAQekD/+TLIU7+uBP+RBGHUrIo4hS92+iKzuLWSz/69OKQ0VlNC/rAl7HgAiYjJZAMVqsuVGmsgCAjYBNwI3pJQGG1QtV24D2Xv8BLL3+FSy87wlL/e6coPRIyUippK9NkcD88ie12ay12bbaD72aMrP7X3J3v9zyAJDNgEXp5T2jFEdlpIFrqwkW46kkez8bgSuTCltqLK844FzyN6Th8g+D/6WUrp9hOr6ILKlPTrJgsz+mFK6a7hl5+XPJwtwWEZ2LlrJlpy4HrgqpTRUEEH/8mYD9wOOIHvvJLLghfXA9dWe29H0sElP8h9ESdKE9sbbrhrvKmiCe9eq08a7Cprg4owTx7sKmuDSFdeNdxUk3cNd1PXtqpf/NOhAkqQxNljQgSRJ48mgA0nSRGfQgYbLoAMNl0EHGi6DDiSNt1qCDlxeQZIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNWkcbwrIEnSPU1K6bzxroMkSZIkSZIkSdJIMNOBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJo3jXQFJkiRJkiRJGgnvWnXaeFdBE9wvN1813lXQBPeoey0a7ypogusc7wpIUg3MdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUgaNxGxPiJSfnnGeNdHGm0R8dZCm//9eNdH9SEiziu0izTIfs8o7Ld+DKtYdyo9Z5IkSZIkSZKk0WfQgSRJkiRJkiRJkiRJqknjeFdAklSdiHgr8Jb85sUppfPGrzbS0CJiJXB7YdPRKaX141MbSdI9QWs6zHpuZAd30cohGmliFvNYwWrmxeKay+1I7aznJraxicMcpIEGZjCbIziGxXHEkMdvTXdyJ7dygL100skUprGI5axkLY3RVHO9NPLqtQ2llNjMerZyBwfYSzvtTKaZacxgLos4imNpiIaa66fRsTNt5Q7WsZdddNJOM1NZwFJWchzNMWVYZe9Lu9jAzexmBx200UQz81nMSo5jWswY8vjD6RB3sI4d3MVhDgKJyUxlFnNZzJEsimXDqp9qV6/9UNHGdAs38w8ApjCN+8ejaq6XRt6WbR289yO7+emvW9i0pZPZMydx1r2aedlz5/CQc6dVXd5N69r4zo8P8LerDnPzre1s39nJgZYu5s5u4NQTJ/PUJ8zk6U+cyaRJMeDYw4e7+OmvD/KL3x3ksr8f5rYN7bR3JBYvaOS+Z07hPy+YxXnnVF8njZ7WzoPc1nIF21s30NrZQuOkycxuWsRR005lfnN1fQVAV+pkV9sm9rZvY2/7Nva1b6O16yAAp899NAubVwx6/P72Hexp39JzfEvHbhKJJVNWc+qc82t6jhpb9fZ9qCt1spvt7GU3+9jFPnbTxmEATuP+LIglw6qTRl69taE/pZ/l35+HdgJnsixWDquOE4FBB5IkSZKku439aQ9X8gfaaQOggUbaaGUHd7GDu1idTmJlHFd1uYfTQa7gYg7R0lNuB+3sZnt2Sas4Lk4ve/wN6Qo25TF4QTCJBg6yn/XcyFbu4Mx0Hs0xtYZnrJFWr22oNR3iKv7MfvYAWTtqoJFWDtHKIXaznWUcRQMO2tST29MN3Mp1PbcbaOQQLdzBOrZwB2ekBzAjZtdU9ua0nhu4gkS22lQjTbRyqCcw5dR0P+bForLHb013cj2X00kHAJNoIIBDHOAQB2inlUUYdDAe6rUf6l9WsW2rvlx9fSsPfeImdu7uAmDWzEns2NXJTy86yM9+fZB3vX4+r33J3KrK/MHPW3jz+3b13J46JZjcFGzb0clFFx/ioosP8X9f3cePv7KMWTP7Jlh+7AV38Zs/HOq53dwcNDUGd2zu4I4fHeDbPzrAS58zmw++Y+EwnrVGyv72HVy260e0p2wAtjEm09Z1mO2tG9jeuoE1M+7LqhmV9RXdDnTs5ordP6m5Ttfs/Q37O3bWfLzGVz1+H2phP3/nT7U9IY25emxDk2mmi86y5XbS2fM9exbVfeZOVAYdSJIkTQAppS8AXxjnatSFlNLvgYHTZyTd43WmTv7BX2injZnM4UTOYkbMpiO1cxvXs5FbWMe1zExzmF/FzJWUEldzKYdoYQrTOIl7MycW0Jk6uYN1rOMa7uQ2ZqY5LI9VA46/M93aE3CwhpM5ktVMigb2pB1cy984RAtXcyln8aAROxeqTb22oY7UzhVczEEOMJ1ZrOFk5rGYSTGJztRJC3vZyiYmYZaDerIj3dXz4+gK1rCKE2iMJg6kvVzLZRxgD//gL5ydzmdSlRkq9qc9PT+OLmEFx3Iqk6OZQ6mFG7iCXWzjai7hnPQIJkdzybpdy19JJJaxkqNYy/SYCUBbamUPO3oGpjW26rUf6u8mrqKTDmYxj33sGnJ/jZ1Dh7r45wvuYufuLu51UjNf/NgiTlzbzL79Xbzjwl1c+Mk9vPE9O7nXyc2cf17lgWonrJ3Mu98wnwecPYUTjp3M7FlZv7V9Ryef/8Y+3vQ/O/nT3w7zirds57MX9s3G0dGeWLOqief8+ywe/bDpHLdmMgC3rm/nDe/eyXd+fICPfHYvxx4zmRc8o7ZBI42MztTBlbt/Tns6zMzGBZwy+6HMaJpHR1cbtx64jPUH/8EtBy5lVtMCFgyRnaC/xmhmVtNCZjctZHbTIq7a88uKjw0mMbNxQX78IrYevo2dbXdU+/Q0Dur5+1CWRWhufpnH1VwyIs9ZI6te29C94yGDln1V+jM7uIuZzKk5IGKimTT0LpIkSZIk1b9N3Janim7kVO7X8499YzRxbJzKwnzG7jqurarc7WzuGVA5lXOYEwsAaIgGVsZajmQ1ALdyPV2pq8+xXamT27geyH4gOSrW9vwQMicWcApnA7CXnWxPm2t52hpB9diGuh8vCziYyVk8iAWxlEkxqaeMWTGPNXFyyR9TNX6628lClnFsnNqzjMqMmM1pnNMzQ+vOPiuRVeY2rieRmMVcTuSsntd+akznFM6hmal00M56bhxwbEdq5/r8x9WVHMcJcWZPwAHA5GhmUSznqDi2lqetYarXfqhPWWkz29nMQpYxn9qXetDo+PSX97Hhzg5mTA9++KWlnLg26x9mzZzE+9+ygMc9YjopwRvfXd2s8cecP53XvmQuZ585tSfgAGDhggZe8+K5vPbF2SzOr3//AO3tqc+x73z9fK77wwpe9cK5PQEHAMesbOIbn1rMg++fZXv6wCd21/ScNXLuOHgdh7v20xBNnD73UcxomgdA46TJrJ11PxY1Hw3ALfv/WlW5Mxvn8+BFz+KseY/l2Jlns3jKMVUdf9/5/8I5C57MSbMfxJHTTqR5kpmdJop6/T40g9k8kMdyejyA1XEyi2L5MJ6lRlO9tqHBtKVWdrIFgKUcVXW9JiqDDiRJkiRJdwtb2AjAEo5kSomlCo4iG0Dbzx5a0v6qy53HYmbGnBLlrgWgjcPsYluf+3axjTZaAVjBwAG8WTGXeSzq8zgaP/XYhtpSayFTxik9P7Kpvh1IeznAXqD39S2aEtNYwpFA9e/99tTGDu4CsmCmiL4JoBqjkSNYlZd9Byn1HfzbzHraOEwzU1nFCVU9tkZfPfZDRR2pgxv5Ow00sJbTKn58jZ2vfS9rF099/EyWLx2Y6PhVL5wDwJXXtHLTurYRe9wzT8vW0z58OLFrT9900+ecNZWGhtLJ6iKCpz0pC3y6fWMHu3aXT1Wt0XfXoZsBWDplDVMaBq5jvnL6aQDs69hOS0flQSIRMeDzqhoRDmVNRPX8fWi4bVJjo57b0GC2sJFEIgiWUF1WmInMnlqqQURMjojzI+I9EXFRRGyIiJaIaIuIrRFxWUR8KCLOGqXHPy8iUvelsH1ZRLwmIv4cEXdGRHu+z3llymmMiH+NiC9FxI0RsSsiWiNiU0T8JiJeHRHzaqjf2RHx2YhYFxEHI2JHRFwVEe+NiDW1P/OKHvsLhXPzhRJ1ujEi9uf1ujoi3h4RAxbUiYhJEfGUiPhJRNyVv7Y7IuL3EfGcqPKbbkQ0RMSTI+IrEXFzROyNiEN52/lFRLwsosR//X3LWJ+/3m8pbH5gsS30uzxjiPIeGRGfiYgbImJ3RBzO283vI+J1EbG0wuf21sJj/r6w/bSI+H/5a78tIrqK7bVEObMi4vkR8Z287ezJ2/CuiLg8Ij4VEf8S0Xf6VkR8tPD4N0cV3xYjov/5O22I/Wuq43BFxJqIeHNE/DF/jQ7nj31DRHw6YohcTsN77CkR8fTCc94fEZ2R9Xkb8zp9NCKeGBHT+h371vw17x9menuZNvv7MnVYEhEXRMTnIuJvEbE9f08eyOvws8j6q/kVPqeV/R53Zb59akQ8MyJ+HRF3RNYfbsvfEy+LiClVnrumyPqLiyJic/66bYysf31O//NVQXnPKNR5/SD7lXtProqId+fvyV2R9UHrI+KrEVF1Pu+IWJu/x6+NrE/bH1kf+4WIeEBhv5L98nBEmc/BEvuVPGd5m3pD3p62RW//972IePxI1LFMfUa0LQ+zLssj+wy8Mm8PByPiloj4VkT8U0TWl5ZrT2XKHPHvR/n7r/vx31rY/qjo/f6yp1z9ImJaRDwuIi7My9qUt/3DkX2+/zmy7ycD/3OtrH5nRsQnIuKm/HXcExHXRcTHovCZUu55DFF25K/Fp/L32Y78XG7J6/2WiDiilnrfXXWkdvaR/fA5n9Lppmczn0ayAdvBBlT62832vNzSszmnxFSmMyvfd2DQAWQzaUoNHhXruyt/HI2Pem1DW7mTRBdNTC5bL9Wf7te8kSZmU/rf6nl5e9jHLjpSR8Vl72FHz5qz88q0qe620sZhWug7MN39g+wilvdkzFB9qNd+qOg2rqOVQxzN8Uyp7l8ajYH9B7q44uos2LHc0gn3PWMKs2dl7/3f/unQiD32JZdnZU2bGixaUF166/lze/fvNOZg3HR0tbGvI+srFjQfWXKfOU1LaIwsW8XOtk1jVjdNTPX8fUgTw0RtQ3exAYAFLL1HZaMbGOooaVAR8WjgS8CAgercovxyJvCyiPg+8MyU0t5Rrte/AZ+A/D/Eofd/OPBRoFQQwLL88mDgDRHxypTS/1VQZlNe5vPou9b2VGA+cCrwX3l5/1tJPYcrIiYDFwIvKnH3yfnlGRFxXkrptvyYI4DvAPfpt/984IH55WkR8eiU0oEK6nAW8H/ASSXuXpFfHg68KSJela/bPmoiYjXZuvD3K3H38vzyQOC/I+IdKaX/qbL8RuBdwKuoILgtH9h6OfDflH5fzQXOyC/PAzYAKwv3fwJ4cX59DfAg4LcVVvf5het/TSldNUp1rElEzAA+ADyLgZ/ZzcBs4DjguRFxEfD0lNLW4T5u4fHPBr4KHF3i7mn55Ujg/mSvwbeBJ4/U4+d1+D/gAkq3pSZgel6HRwJviYhXp5Q+UcPjnAJ8Azi+310L6X3fvzgiHt7dVwxR3gnANxn4vj8yvzwYeGVEPKnautYiIl4CvJ+s3RQdlV/+LSI+A7wgpTTkTzwR8UbgzcDkfnetzS8XRMRngZcMt+4jLSKeDHya7P1TtBx4PPD4iPgp8KSU0oj9AjdWbbnCujwL+BAws99dq/PLk4AfR8QFVZQ5Jt+PImIh2WfYoyrY93nAB8n6qlKW5JdzgFfnbfalKaXWCspuIDuHL6Lvdx7I2tYJwAsi4t30DRasSP7d4RNknyv9Lc4v5wCvjYh3ppTeXe1j3B0VfwCYXuYrcUQwLc1gH7tpYV9F5balw7STzQKcMchX7RnMooV9A8rtrle5OmX3ZW/HdlppS633qB8l6km9tqG97Mzvn02ii9vTTWzhDg7TQgONzGIuR3AMC2NZRfXR2Oh+Haczs+wsumJ7OMg+ZpX5IXVg2VlbncyUsv1FsQ23sK/nsTpTJ/vZA8BM5tKS9nEbN7CLbXTSTjNTmcdiVrKWqTG9ovpo5NRrP9RtX9rNHaxjOjNLZu/R+Lvhlja6J2KeuLb/v2uZSZOCtcc08be/t3L9zcPLdHDoUBcbN3Xwte/t5/99fA8AL3zm7KpnD//hkuxfr8ULG1gw32Co8XKgkLlgRmPpz6SIYHrjHPa2b+NAx66xqpomqHr9PqSJYyK2oQNpb8/37XvS0gpg0IFUi5X0/UF9H7AO2As0AEvJfrDv7gEfD6yKiLNHcvCiKCKeQDYwCJCAG4CtwDyyAcn++7+ALDigGHa8E7gFOAQcQW8wwhzgcxGxNKX0rkHq0EA2YPeEfnetA+7MyzmZbMDrYxExcvnbBvcZ4D/y6zuAm4CuvC5z8u1HAr+NiBPJBib+QO8g63qyAeSpwGn0DrA9EPg82cBMWRHxMOD7ZINJ3VqA64HDZG2lO6PAfODzEXFkSukdJYq7mGyQYTXQvfDZbuBvZR5+QLhxRJwKXEQ2kNqtDbgW2E/Wvrs/CacD742INSml55R/lgNcSO9AYytwHdn7ZCn0zYGUB6p8lYHncR9Ze9xLFkizlt7BsTnFHVNK10fExWSvCWSBBEMGHUTEAvq210+W2W/YdaxFRCwGfg7cq7C5C7iR7P09lWxAuzvX3cOASyLiASmlO0fg8Y8DflUoH7I2chOwh+y9vICsPXbn+O3/y8A64Jd5XR9Q2P4Hsr6mv6tLbDulX7kbgc3AAbI2eizZe4f89scjYk5K6T3ln90Aa8n6rzn57VvI3j/TyIKlur91rgZ+FRGnpJQOlissnzH9O8hzZWfagGvyeh9NFmx0HFlb/a8q6lq1iHgd0H0+Wsne7/vo29cDPJesn3zDEOW9F3htv813kb3ek8kCN2YBzyE7h+3DewYjJyKeAnw9v9lB1j/tJHutTqT3s/ufgM8B/zaCDz8WbXlI+XeAj/fbvJPsvZ3I3g8LgMcAPyT77KnESkb/+1Ez8FOgO1PCLuBmsja2qsT+x9I34GAnWeaVfWT91gp6P/MmkQWNrYiIR6VBcublgWhfAZ7S764NZN8bppH1z1PJgtWqmuYVEY8ja6fFKfH7yb7fHSD7LnAC2bmcCrwrIlamlJ5XzePcHbVyuOd6M+WT0zQzFdjdZ//Kyy2dqQCyHyr671+8PXSdMm0cZvKAGDGNhXptQwfJ4pwbaORyLmYfuwiCBhppp42dbGUnWzkyrWbt4Im7NIa6X8fJg7zmxfZQaXvK9j2UH1++nTZEA42piQ7a+5R9mJaeGV0H2c+NXEkXnUyigWASh2hhE7exhY2cms5hXiwq9xAaBfXaDwGklLiRK0kk1nIvs2TUqbu29s7wXLak/E//Sxc3Aq199q/G5CPWDchI0NgI/3nBbN75uuoSt226q4NPfSmLRb7gX8sPKmn0tXX1/tTRPKl84Fn3fa2dZX8akYD6/T6kiWMitqHNrAegicks6Bn6uWcw6ECqzd+BLwI/TSmt639nRCwBXgq8mux9dirZzO9XjFJ9vpD//QzwlpTSXYW69AnriohHAv9L74/+fyIbYPpT8Qf2yJZBuBB4dL7pHRFxWUrpV2Xq8F/0HcD9K/D8lNI/CmUuIhv4ehbwEbIBn9H0aLIBnDvJZmH/OKXUldeliex5vzXf9yiy2YoPIhsQ/AvwkpTSlYX6zyfLWPDYfNMTI+LclNIfSz14RCwnG8js/pZ+GHgj8MnuAct84OIRZLMZuwc+3h4Rf08p/aRYXkrpgvyYt9I7a/LqlNIjKjkZ+az579IbcNAF/A/wvpTSnsJ+ZwOfIgvMAHh2RFyVUvpYBQ9zOtng/6H8uX46pdRSKHt1v/0/SN/B/GuB1wG/TKk3F1Jky1mcTjYA+MQSj/txeoMOHh8Ri1JKQ+WZfAa9g8l7yGallzJSdaxYni3iO/QGHBwC3kHWdnYX9msCnp7XcRZZ2/1qRDyou60Pw3voDTjYCrwQ+FHxOed1mAycS5bhoM+3v5TSV4CvRLZ8QXGJhQtSSusrrEcb2fvoO8CvS82Kztvse+kNbHhHRPyy+P4dwlfIAg6+C7yu2K9HxGyyvvBZ+aZjyLJelAzCyl+7r9MbcJDIMgy8p9/77Fyy99nxZLOlR8vJZOflEFmf9+liwEREnE52fruDD14dEZ9KKW0oVVj+GVIMOLgNeAFwUfdnSN4mLiDL0vFvUDe5wheQBYt1krXvD/R7TVaTtYXuLDdPjYj/TSn9eYQefyza8qDyjB4fLmzaRvZ95TvdGS7yIMJ/JvuucC4lghcHMdrfj15EFty1AXgZ8JNiZo4SnzGJLGjia8DPU0p3lKjTKrI23T1g/wiywLmPDFKP/6RvwMHfgf9MKfUEAUbE9Lyct5N9ZlQ0BSiyJRm+AT3/8XZ/5vyi33NdBrwTeGa+6bkR8beU0mcreZy7q67CV8tJg8R6NOT3dVb4VbSz4nIbS5bbXa9K6gTQMepfkVVOvbahjjx+r3u90aM5nqM4lsZooi0d5hau5S7WcwfrmJXmsjTuWbNp6lX369gwyGtebA+VtieALjoHHF9KAw100N6n7I5CPOh6bqSZKZzA2cxjMRHBnrSD67mcgxzgGi7lnPQImqL0bGmNvHrthwDu4Fb2sZslrDAYpY61HOyNnZ06pfzg/bSp2X0HWmr76WDJogY6OmDf/i4OHc4e8z8vmM1rXzyXpqbKgwY6OhJPf9EWDrQkVixv5HUvKZc4TWOhI/V+RkyK8kNHDfl9nalu5hioTtXr9yFNHBOtDXWlrp6lzJaw4h4XpHnPerbSyPhCSun0lNKHS/2gDpBS2pJSegPZYGC350XEnFGq00zgbSml5xUDDvK67Eop7YKeQecv0htw8CXgvJTSH/vP6Esp3UI2uN6dQSHIBt4GyFMdv72w6a/Ag4oBB3mZ21JKzyYbYJtC3xnUo2E+2YDK/VJKPywOwqaU2lNKbyMbYOr2DrLBhouBB/cf5Ekp7SQbWC0Onj6T8t4PPbl8uoAnpJQuLA74pczPyQZ2ipkJPpUPKI+kN9CbIQHghSmlNxQH3fI6XZLX55rC5vflmQGGMpPsuT4mpfTBYsBBXnZxMPfB9F324pfAvVNKP+0/sJ1S6kopXZ5SegXZ7M7+vg90t/0mBn9duhVnhH6p1EzbEa5jNV5JtmQBZDNyz00pvacYcJA/Znu+9MkDge529QDgX4bz4PnA+SMLm56eUvpe/+ec16EtpfSblNLzqey8V+v8lNJTU0rfLZeGPW+zDyGbAQ3ZrOJXVvEYC4BPpJSe2L9fTyntzfutXxc2P4vynkff7BSvSim9tsT77I9kr9U6+mYeGWnzyAK8zk8pfah/hoa8n3sUWQYEyAaC/4MS8iCp4kDsHWRt81fFz5C8TXyGLFtAB6P7/Koxneyz599TSm8q8ZqsI2v3xSCJwV7rao1FWx7KB+nNTLKf7LPum8XB7JRSZ0rpu8B5ZFkKKn39xuL70Uyyz8ruz/U+c6tKPO7bUkrnpZQ+XSrgID/mtrz/en1h8yvz4IsB8u9SxewTVwEPLAYc5OW2pJTeSxaAE/RmsSgrD177Kr0BB78Bzso/c/o/180ppWf1q8t7IqJ86H/fx7qi3KWS4yWNtd5/1ZawgmPiRBrzfxUmxxROjDOZlSebWc+N41JDTRyJPv/6cyJnMT+W9MwsnhMLOIWzAWinjU19/v3VPVVrOsStXEsjTazhlPGujurAxiuPZvPVR7P/tlXcftlRvOI/5/DJL+7l1Adv5OK/VJ7o9aVv3M7Flxxm8mT4yscXM3tWVUnCJEmqK7vYSlv+M+s9bWkFMOhAqlpK6UAV+36DbMY8ZIMdDx+VSmXpoUul4+/vOfQOHtwKPK//j9hF+SDSCyFfgAZOzAdh+3sGvemLO4FnlxrALXgd2ezYsfDqlNLGQe4vrpc9maz+z0xl1nPOt3+usOn+pfaLiKX0ne3+qTy4oKR8MOSlhU3LyAIcRkRETKHvIPvPU0qfGqQ+e8kGkLt/kZpKtmxBJT6dUvpNBfu9sXB9M/DUIdpNd90GvAdTSu1AcXbnc2OQfHwR8SD6ppUvubTCSNaxUhHRTDaTvtvLU0qDDgKllK4imx3d7SVldq3UQuiT17lkNo8S9Sjbn9Sq0nOZB0QUz9tjyw0alrCeoZc4eH/h+qp8lnEpLyxcv5RskLeklNIOsiwBo+39KaU/DVKPdcD3CpvOLbPrw8jS43d7RUpp8yDl/pFstnw9+VpKqVxWE/LAnmJfUu5cVG2M2nJZ+bIfxc/wt6eUrhukHjfSmw1oSGP4/eiVKaUBywcNt07A+8gyI0G27MKZZfb7N2B24fbzUkr7y+xLSunrZMtUVOKx9Aat7QaeklIaKnffm8iWhIEsgOqpFT7W3dKkQiK/7hkLpXTm9zVUmPivoeJyu2dg9C23u16V1Amg0YSE46Ze21Dx9oo+H8UUtmdfbVvYT+vorOqnfv6WfsMf0o8HXDakm4DijPHyr3mxPVTanqB3NtZg7an42MWyi9enM4t5sXjAcTNiNvPItu9iqARyGkn12g/dyFV00sEqTqQ5yqcg1vibPq33p5DuDASlHDyU3Tdj+vCGByKCFUc08f63LOADb13Art1dPO1FWzh4cOgMCm98904+9aV9NDTAl/93Cfe7d0XxsxpFjYX5T10D55306Mzvaxjx+VKaaCbq9yHVj7tbG9pMljx2BrOZFfe87D0GHUij75LC9XuP0mN8tsLBvmcUrn+k3MB6UUppH9ks8m4PLbFbcVmF3w02iJGX2UqWVny07aN37e5yLoc+nyoXpZSGmspxaeH66jyVeH+PoXc2KWRpxofyffoGYzy+gmMq9QD6zrIcsj75QPfva6jPkK9tnmK7OPj1wf6z+GvwKXpfy2Mo3Va7FQMo/pBSumGM6liJRwLdv/xtI8tIUokvFq6fHRHTyu45tP6/VN+r5F51JmUZWnbmN2dQecaJz6SU2obY509kWTy6ndh/h3xQt7j9Y8UMAKWklH5Ntk77aPp4BftcXLg+4LnlHl24voW+nw3l1FvQQSX1KZ6Lcn38qBpGWx7MPxWut9E3gK6c/2NgfzBSavl+tINsGZQRl7JsSH8tbCpXp+L74PKU0mUVFF/J8kTQ93vaF/LApEHl3/+KWZsG++wrHndGuUuFda1LxXUYB1trsZJ1G/uWW1wfsvxboi1/zP7lNg+yPnapcidXWC+NvHpvQwDTmFny2OL2w6PWdauojdaSl+4lUrpf97ZBXo9ie6i0PRXLHqyddqbOnqUUimUX22O59gQwPb+vlYNl99HIq8d+aFfaxnY2MZ1ZLOMoOlJHn0sq/JvUva1r2Kv9qVbLlvQOiGzeUn7Q+K6t2X1LF4/cINxznzab5uZg85ZOfv7bwfuOd39oF+/96G4i4FP/bxFPfPRoJ0NVJZoben9Kau1qKbtf933F/XXPNFG/D6l+3J3aUHtqYwfZ/Kx7YpYDwNAeaTjyZQUeRrYm8TKyNdWb++1WnIpyxChV5Q9D7ZCnLj65sOmiKsovLpPQZ+ZfPhhTHJAsO5u/n58C/1NFHWpxRT4DvqyUUltE7KI3A8Qlg+2fKy5hEWQzHvuvWX524foNKaVbhyo0pZQi4kf0zrg+e5Ddq1Us6wDwuwqP+yHwoPz6qRExrX969n720be9lPPAfre/VWF9ykopbcrPX3dwxPMp0c7z920xgKJckMSI17FCxcf9fSqxpEEpKaWNEbEHmEP2+X4avTOJq5JS2hMR6+jtv74WEc/NB8jHTUTcC7gf2QDsPLJU6/1ngE8vXD+CvsuElPPnoXZIKR3M+4ruZUbmlNjtPv1uV9MfHl/hvtVaX+Gs8DsL1+eU2af4/C6uJNgtpXRLRNwBHFlBHUZbO1DJAHHxXJTr44dlFNvyYIqv398rCaJKKe2LiMupMuPDKH4/uqTSPrFEnVaQBZKdQhbYNZMsw1FR8XtSuToVz2MlWX0gyxbTTt9gxP71C/qe5xH5nnZPM70weNbCvj63u6WUOMiBfP9ZFZU7OZppSpNpp40D7GM+S0rud4B9JcudwSx2soWW/P5SWsgSZjTRzOTo/3bRWKnXNjSd2exgS0WPpbFz/3jUoPd3t58W9pNS6lm+oOhAoV+otD1B1q9ANkDcllpL9hstZcqeHM1MTs09aV+HVvna7Bq+euyHDueBJy3s4/eDJHA6zEF+zw8AOIEzWcbKiuqmkXXc6slEQEpw3U1trF09MIa6qytx063Zz2UnHDtyMdbNzcH8uZPYvKWT2zaU/znuQ5/aw5v+Z1d2/R0LeOZTKu//NLqmN/TOyj3QsYvpjQNn6aaUaOnYA8CMxnkD7tc9y0T9PqT6cXdqQ1u5gy66CIIlrKi4HncnBh1INYiIo8hSbT+e6t5Hc0alQpUtVXAyfbObfCQiKk2Fvrxwvf/azivoO5BQ6aDITQzxA/wIqPSXueIgeiXH9B90LxXWWxxMqWQQvtvVhevLImJqJen8K1Csz7X5jM5q69MIHMXgs7JvH2pmd644wLp9iCUwqvFxegMKHhcRS1JK/V/TZ9I70LQD+M4Y13EoxQUy7x0Rv6ji2GK4ZaXrsJfzPuDT+fWVwEX54PEvyAbQLs1nY4+6iHgs2fIR1Q7Mz6lwv0r7ihZ6gw5Kve+PLVzfnFLaVWG5wx1MHkw1z61buakKxRDdarIzXE99BB3sHCoQLdd/OseITd0Yg7Y8mOG8fhUFHYzB96Oql2aKiJOAC8kyAFQzajKgTvlSRYsKmyo6jyml1oi4DVg7yG5HkAWgdHtjRLyskvL7HTfcvn9Ca4wmZqW57GM3O9nKoj5fYTN72dUzS2Fen5dzcHNZxDbuZBdbOapPd585nA71/BjRv9y5LGQDN3OAvbSmQzTHwNTBO9ladZ008uq1Dc1jERvI0oseZD+zGPgD/0F6V3qZOnIfXRqGufnr2EE7+9jF7D6J5zK78vf+bObREJV/dM5hAUGQSOxiG0tKfNXq7leamTJg4Hoei9nCxj7tpr/u9jjF9jSm6rUf0sQxc8Ykzjy1mcuuauXXfzjIE/5pYAaBv155mL37sp+FHnz/kVvS4EBLF9t3Zj81llu24RNf3Msr35ol9HrPG+fz4mfPGbHH1/A1TprMrKZF7Gvfxs7WO1k85ZgB++xt30pHnixy/uSBfZRUVM/fhzQxTKQ21L20wnwW32OXozLoQKpSRJwF/IraBgBGa9pS+WlTvfr3xhWl3y1hdr/b/UNed1KBlFJHROyldwBvNAyVLn2kjik1iFE8L9XMkO2/71xGJrX1SNZnMJW0Reg7QDKSi4T+BriZbPC3EXgW8O7uO/OZpM8t7P/5QdLqj1Ydh1J8r67ML7Xo/16tSkrpMxFxNPA6etv4kWTn77kAEbEZ+BHwuZTS5cN5vHIi4p3AG2s8vNI+t9JpXkVDve8r6gtr2LdatTy3cuYUru+p4rixWJakErWeixGZ3jdGbXkwcwrX91RxXEWv3xh9P6r0MwaAiPgnsuUYajl/pY6Z0+/2nirKG+o89v+edr8qyi4aVt9/d7CEFexjN1vYyKp0/IAB/g3cDMBM5jI9Kv/RaQlHso072clW9qc9zIw5fe7fmJc7mSk9P4p0m8ciJpPNKt7AzRzLqX3u35/29PxIUupHDo2temxDc1lIM1Np5RAbWcdJJVaA2UgWCzqLuUy+h/7AVW9mxCxmpNkcYC8buJlT+iWya02H2MIdAFXPhGqMJhakpWxnMxu5mcXpiD6zvjpTB5vyWL3FHDlgRthSVrCFjbSwj51pK/NjcZ/7D6S97Mr/BVpQZka8Rk+99UPLYuWgWQtuTddxOzcwhWlDzlbU2Hjq42dy2VWtfO17+3nTK+YNWELhA5/YA8AZpzSXzIRQTkdHorGx/L9HH/nMHtrzMO/732fgZ9EXv7WPl7w++5npTa+Yy2tefM9b63oiWDplDfvat7H58M0cM+NMmhum97n/9parAJjVuLBkJgSpqJ6/D2limChtqCXtZx/Z/LOl9+BsT6VDDiWVFBHTge/R+6NzO9k6uk8hyyQwD5iSUoruC/C20a5XhbPWpw+9S0X69xv9/zupZtB+JAfD6k1xsGI452SkfjEcq/pUmkGhWE75RZOqlGdZ+ERh03MjothmH0xv1odE70z+UkaljhUYrfdq1VJKbyBL1f1VyPN39rUM+E/gsoj4Vr6My4iJiMfRd5B2E/B2sqCpVWTp0Rv79bkbRrIOVSr2h/aF6jEB23JVxvD7UcWLA0fEcuCb9H7+HQQ+SZaF4fi8rs396vTFGuo0Ukaq77/H/4qynFVMYRqddHAVf+ZAymJVOlI7t6Sr2U624sxqThxw7K/Td/h1+g63pusG3LeQZT2zy6/mEvamLF6sK3WyId3cM+B7DCcwKfp+BE+KBlZxApANDG9IN9OVr1CzJ+3k6nx1r9nMZ2EsG/Y50PDUZxuaxJp8BZgtbOTWdB0defKetnSY69Pl7Mtjm7rbmurDak4CYBubuCVd3fO6HUj7uIo/00kHU5nOco4ecOzmtL6nTR1KA9fVXsUJBME+dnMdl9GWsq+Uh9NB/sElHOYgjTSxkuMGHDs/lvTMZL+ey9iZttKdsK7YL01luinyx0E99kOaWJ739FkcdUQj+w8kHvv0u7j+puzf0/0HunjtO3bw/Z9lfco7Xz9wtmjD0nU0LF3H2/7fwNj4kx64kY99bg+3rm/v6TMAblrXxn/993be/L5ssOWfHzmdk4/vG8P73Z8c4Lmv2EZK8KoXzuGtrx742KoPR047kSmTZtKZ2rly98840JG9rh1dbdy0/y9sa80G4NbM7L/CJPxyy8f55ZaPs27/30qW3d51mLauQz2Xbp1dbX22d5VYzbEztffdh2yfrtTZZ3tHVyUJDjWW6vX7EEB7aqMttfZcunXS3md7V8VJgzUa6rkNdbuL9QA00sRClg7n6U5oZjqQqvNMetf4bQcellK6eIhj6iVvz55+t+dVsp5zBfrPOqzm+d6dF1LaU7g+nHOyp9RONSiWUw/1Kba9kZ6V+QXgXWQp0VcC55MtCQDw/MJ+v0kprRuknNGs42D2FK5/JKVUaXrtUZFSuhJ4WkQ0AWeRzb49FziPvm3pScAREXFuSiX+O6zNmwrXLyPrc/cOccx49rnF/vDu2BfuAbqnwc2p4jinPtRHW95TuD6niuMqef3q8fvRy+kdyN8LnJNSun6IY4aq055+t+dUUZ+hzmP/sk9PKf29ivKVa4gGTk3ncCV/YD97uJRf0ZAa6aSjZ5/VnMT8qG7mbkRwSrovV3Axh2jhMn5HQ2qki04S2Y/uy1nF8lhV8vgj4hj2pz1s4nZu4WrWcQ2TUkNPvaYynVO4b43PWiOpXtvQkljBgbSX9dzE7dzAem6kMTXRXohzXMMpLIh77g9c9WhBLGVVOpHbuI4NZIO6xfd+E5M5lXOYFA1Vlz0z5nB8OoMbuIItbGQLG2lMTT1p9xto4BTOLrk2LcBJ3Icr+QMH2Mvf+SOTaCBS9NStmSmcyjlVpajVyKjXfkgTx9Spk/j+F5bysCdt4sprWjn5vI3MmjmJAy1ddHVBBLzr9fM5/7zqlk+55bZ2XvbfO3jZf++guTmYOT1oOZg4dLg3AOERD57GFz+6eMCxr33HDjrzXwq+/O39fPnb5Zd3+c7nlnDOWSO37IOq0xCN3GvuI7l814/Y17GdP+/4Bo0xOR/ky17rNTPuy4Lm6tcr/8uOb3O4a+Br/4+9v+pz+6y5j2Nec9+lG24/8HdubRmYZHNb6+1s23Z7z+1lU9Zy8pyHVF03jZ56/j70V37N4QErKcM1/LXP7dN5gEsPjaN6bkMAKSXuIluZeQlH1lSPuwv/c5Cq84jC9a9X8IM61Mc61jBwXe/FjEzK6639bh8N+bSIQUTEAuonIGM0FNPxD1wArbzivm2M3CD/SNSnfznDcVfh+oqImJJSGpFsAimlPRHxdeDZ+abnA7+IiEXAPxd2/eR41XEIxffqwP/Ux0lKqR34S355f0RMBh4NvJPe9enPBv4V+NpwHy8iFgJnFDa9dqhB2oiYQW2p3UdKsT88MiIaKgzAmCi/6m2gt00eP9iO/dyjp1zWUVveAHRPRRnp168evx8V6/ThCgIOYIg6pZQOR8Q26PmloaLzGBHNDP0+L/U9TTWaGXO4bzqf9dzIDu6ilUM00cxs5rKCNcyL2k7vlJjGfdJDWc9NbGMTh2mhgUZmMocjOIbFccSgxx8fZzAvLeJObmM/e+iik2nMZBHLWclaGqOppnpp5NVrG1odJzM3LeQObmUfu2inLU+DvoAVrGF2OGu0Hq2K45md5nEHt7CXXT0zsRawlJUcN6z1XpfFSmakWWzgZnazg3baaGYq81nMSo5jWgxcy73b5Gjm3unBbGQdW7mDgxwgkZjOLBayjBWsGfTHVY2ueu2HNHGcemIzV/9+Be/9yG5++usWNm3pZP7cBs66VzP/9bw5POTc6gIOAH7wxaX89o8H+ctlh9m8tYPtOztpagxWH93EWac182//MpNHPaR0Aq+uwiThrdsH/ze5rT0Ner9G36ymBdxvwVO4reUKtrduoLWzhcmTpjC7aRFHTTuV+c32FapOvX4f0sRRz21oF9tozVfIXspRNdfj7sCgA6k6xR6jdJ6ognz9+HNGrzpVuRpooXfW39nAjcMtNKW0LSLupHeG432obMBxYA6uu5crgO7FDM+MiKZ80HYoxfby9zJLZxS3VZpG+YrC9VURsTil1D9gZKj6bE0p3Vnh4w2lGJgyOX+c345Q2QAfpzfo4NERsQx4OtD9a/4W4IfjXMdy/kI2mA/0W6SqjqSU2oDvRcSfgevoXY/84QzsA/q340rabf+Q+SH7XLLXaDzzgBbfZ1OBU4BKZipPlP7wr9CziPQDKwmqiIg11E/w3Xipl7b8V+DJ+fV7RcTcoTIeRcQssiVWhlKP34+qrdMM4NQKyv0r8Jj8eqXTZ86l9/OnpJTSzoi4GTg233Q2vVl6VIPmmMJaTmMtp1V8zEPjiUPu0xhNrOaknvSO1VocR7L4Ht8tTgz12obmxxLmU93sZo2/+bGY+VXGky2LlRUtbTAr5nFyjZlSJkUDK1nLStbWdLxGV732Q/0dEydyTImlHjT+lixq5EPvXMiH3rmw4mM671pd9r7HnD+dx5xf26pgt122sqbjNH6aG6Zx/KxzOZ5zKz7m4UteOOj9D1z09Jrrs3rmvVk9895D76i6VY/fh+4fjxp6J9WNemxD3fV6KEN/B7sncIEuqTrVTj96BLB8yL3GQD7g/evCpueMYPHFGY1PzNOwD+XfR/Dx61HxnMwGHjvUAfls2EeWKaOouPhQpfnmimUF8LQK6tNMth73UPWpxRXAjsLtF4xg2d1LAnTnwWoEnptfun0updQx4MC+RrWOg/h54fqKiHjYGD1uTfLglT8XNpX6Fbz/glmVtNtapns+e+hdRtXfgGI2jKcOdUBEzAb+adRqNLJ+Uri+BHh8Bce8aJTqMpHUS1v+aeH65Aof41mMzvt1LL4fVVunp5Odl6EU3wdnRsRZFRzz4grrUOz/L6jw+5QkSZIkSZI07gw6kKqzuXD9AYPtGBHTgA+ObnWq9v7C9XMi4j9HqNzPF64vA1462M4RcTpZCva7s98CtxZuvytiyBw/76V3wCMBnymzXzHt/zH5jNFBpZRuBX5X2PT6fImLwbyGvumdPz3U41QqH/D/WGHTEyNiyMCMKn28cP219C4V0UX5c9tjjOpY6nGvom+A0EfzgekxU0mb6qeYY2pXifv30Hcwfk0FZW7ud3uoPvchwJMqKHfUpJT2A98pbHpRRAy1yOFbqTx4aLxdBKwr3L4wovzC0RFxLgYdQJ205ZTSTfTN1vKmiCi7dEJEHEfWPitRj9+PqqnTYuDtFZb7NaC4PManI6LsclER8RTgcRWW/SGgOyvSUcA7KjxOkiRJkiRJGlcGHUjVKf5Y/8SIeHSpnSJiPtlMuLrKUZhS+jPwjcKmj0XEayNi0KVWIqIpIh4bEb+LiFKL0vyWLCV8t/dExBPKlHUsWVr7u3X/k1JK9B3AWAt8O0/f3Edk3kA2o7TbV1NK6/rvmyumcJ8HPLPCar2dLJgBslT4P42IRaV2jIj/oO9g0x9TSr+p8HEq9WFgY+H2NyNi0JnhEbEgIl5TYfnfAnbm14uDur9IKW2okzqW82p6B+nXAhfnA4CDioijIuJdEfGBYT7+AyLi5xFxfkQ0DPGYjwHOK2z6Xf998hT8VxU2vXCoIJyU0kb6Bu78v7xvLVWH84DvUvlyI6Ppf+gdNJwG/KTcwHxEvBD4rzGq17Dl/VoxqOxI4I8R8bBioEpETI6I55LNrG8Eto9tTetLnbXll9PbPmcBv4uIJxff5xExKf8M/x1Zpp5KXr96/H5UrNOLIqLkMhF5YNBFwFCBeACklA4Ary9sOg34ff+MBxExPSJeC3yJ7LN3J0NIKa0Hiv33ayPiwogYNDApf80eFBE/jIhBAywkSZIkSZKk0TDoQKOkAT5NNmN6Btmg+Q8j4svAj4GtwFyydXufRTaou49s0GXIFNtj6Nlk6wWfDjSQza5/YUR8kyw1+HaywY45+X5nAueTDTxAiYGQlFKKiOcAl5MNsjUB342IH5DN+r2D7Nw8hGxZh6nAH4GjgSNG4TnWhZTSl/IB2e4FfR4NXBcRnyE7V61k5/gCsrWbu60HXjJIuTdFxOX0rrP9uYh4PXAL0FbY9SMppd8Wjvt9RFwIvDLfdG/g+rw+fwb2AyvJllR4RKGcPcB/VPasK5dS2hMRTyYb2JoKTAG+FhH/RdZuriN7D80GjgceSNYWDwHvq6D8wxHxf2QD+EWfrJc6DvK4V0XEs4Evk/U1p5K1nZ8AvyIbwNwPzCRLc38q2Uze0/MivljrY+eCrA08AtgaEb8ga7O3k83wbQJWkS0L8Dh6g4huBb5SpsyvQM/CWOcDd0XEVWTnrzsY5tqU0n8XjvkAvRkrTgCuiYiPky2d0UY2E/hxZGn+A/gZcBIwVHaBUZNSujYi3kVv0M7JZK/dp8n6vQNk5+7f6V0P/mvAv41xVWuSUvp5RLyPLBMKZBlEfgVsjoh1ZG3jBHo/M75GNsh9QX67dQyrW0/qoi2nlK7O+6//zTctAr4J7IyIG8nei2uB7kVf/0i2tE73+7Lc61eP348+BDyD7LvOdLIAmc+SBRjsInvuD8n3mUb2XeUaoJIFHT9J1t93Z206HfhbRKwn+wyfRvbe7w4WeA9wTn4MDP4+eCNwSqEeLweeHhFfBy4BtgCdZN/TVuWPfT69mYnqLcuWJEmSJEmS7gEMOpCqkFLaFhEXkP1A30j2w/oF9A6mFLWQDd7eZ+xqOLSU0sGIeCDwBeBf8s0rGDgwW225N+QD7D8m+7Ed4J/zS3+3kQ00/LnEfXc3TyMbxOlOlb2CwdMl3wg8PKW0Z4hyn0uWgr97tuzq/FL0gxLHvZpsAOa/8tvzgdcN8jh3AY/IZ1+OuJTSX/P2+COywXPIgiHuPchhh6p4iE+SBVl0D4rfQTaYV091LPe4X4uIPcBXyQaXJgGPzS9jaTHl+7mijcCjU0oHy9z/KeAxwMPz23PomyGhe1vRJ8kGBbv7qqWUf/9cSTaQf9UQ9Rx1KaW3RcQSoHsJm7lkA7KvLbH7p4GvM0GCDgBSSq+NiP3Am+hdEmZZfin6HNla9l8obNvLPVPdtOWU0scjopVsUL47+8584H79dv0JWcBZsd2WfP3q8ftRHgD0CrKMNZAFjb04v/S3nSzgo2zAX7+yU0T8O7ADeCG9AZkr80vPrsC7gbfQ9ztP2fdBSqkrIv6ZLHige3mSBXndKqqfJEmSJEmSNNbu1unNpdGQUvoe8FDg2jK7dJLN+jw9pfTzMatYFVJKB1JKTwQeCfyerM6DWQ98Arj/YIPP+az6e5HNIkwldmkjm7l9RkppU9UVn4BSSq1kMyGfAtwwyK47yQYlzshTcQ9V7lXAifkxfyQbMGkb7Jj8uJRSejnZrMjLBtn1ANmA1EkppauHKnc4UkqXkWUJeBfZAE7ZXckG414/yD79bSCb0drts3mq/3qq42CP+zNgDdks2W1D7N5Klk78RcArhvnQ1wBvBi6lNxV7OdvJsjqcnFK6sdxOKaUOspm7/wZ8nyxrQgul+4ruYxLZ++fNZDOjS9lNlrHl7AqCdcZMSukFZEFHd5bZZRPw/JTS88euViMnpfROstnYHwSuJ8u80QLcTJZp44EppeeklA7TOwMb7qFLLdRbW04pfY6sT3snWXDDHrJgqVvJsrg8BnhsSmk3Fb5+9fj9KKX0EbJsQ+vL7NIGfBs4JaV0RZl9ypXdmVJ6MVkA2qfIsg0dJHt9ryfLbHFGSum/88+dit8HKaX2vOyzyYI5h/p830K2jMMjyL4TSJIkSZIkSWMqst9AJVUrX7/6dLIU9/PJBlzuAv6UUtoynnWrVkTMJpvheATZc0lks/DWA9enlDbUUOZRZOnel5ENZNwJ/D6ltGvQA+/mIuJYsgGKRWQzhLeTDU78NaXUNQ71OZLstV9ClgZ6J9nAyZ9TSkMGMYxCfSaRva9OIkvv3UQ2gHMbcEVKaWuV5T0W+GF+swM4KqW0uZ7qWMXjBlm67lPIZr3OIBvk3Q7cRLY0wbAzLJR43KlkyzesJmu304DDZMEX1wJX5QEFoyoiZpD1KceStdXtZH3UxSmloQIjxk3eXu5HFiQ0l6zetwB/HI/3/FiLiEaywJ+Z+aaHpZR+PY5VGncTrS1HxPVkAQoAz00pfXaI/evu+1FENJAt73IaWUaV3WSBPxePRbBSRCwmCwzotialtK6K46eRLc+wkuycTiI7rxuBG1JKt4xcbeFhk57kP4iSJOke7ZebrxrvKmiCe9S9zh/vKmiC69w61NwjSRpdF3V9e8BS60Mx6ECSdLcVET8nm/kJ8L2U0r8Mtr+kkZWnoP9KfrMNWJRSuqcusTDhRMT9gD8VNp2QUhosa49KiIg3kmWVANiSUlo6nvUZikEHkiTpns6gAw2XQQcaLoMOJI23WoIOXF5BknS3FBGn0xtwAPDR8aqLdHeSz2SvZL+VwIWFTd8x4GD8VfH6zQOKWQ3+YsBBryrO4xnAGwub/m90aiRJkiRJkiSNH4MOJEl3OxGxDPh8YdNfUkq/H6fqSHc3742Iz0TEQyKiqf+dETEjIv4TuJxsSQ7IluR491hWUmW9JCK+ERGPztP29xERzRHxVOBK4Lh8cwLeMpaVnAC+GBEXRsTZ+fINfUTE/Ih4LfAHsmU0IFuW5iNjWUlJkiRJkiRpLDSOdwUkSRoJEfGD/OoCsvXEuwd5uoBXjkedpLup6cBz8ktbRNwCbM/vWwAcDxQHYRPw0pTSdWNaS5UzGfjX/NIZEeuArUAHMA84Id+n6F0ppV+PaS3r3zzg6cDLgUMRcTOwi6ztLwaOBYrZENqAC1JKW8e6opIkSZIkSdJoM+hAknR38bgy29+QUrp0TGsi3b11Fa5PBk4cZN8twAtTSt8f3SqpCsXXrwFYm19K2Qu8LqX0yVGv1cRTPI9TgVMH2fdW4BkppT+NbpUkSZIkSZKk8WHQgSTp7iYBe4DLgA+nlH42vtWR7nZeBfwMeChwBrCKLMPBFGAfWQr5K4CLgK+mlA6PUz1V2ofIlr54OHAWsBpYSDZwfpDs9fsH8FvgSymlveNTzbr3VLJz+CDgXsDRZNkPmsiCNbYBfwV+DnwnpdQ5TvWUJEmSJEmSRp1BB5Kku4WUUgy9l6ThSim1Ab/IL5pgUkpdwB/yi2qUUmoBvpdfJEmSJEmSpHu0SeNdAUmSJEmSJEmSJEmSNDEZdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmrSON4VkCRJkiRJkiSpHjx82WnjXQVNcL/c/KvxroImuIc/4T/Guwqa6C69erxroHsgMx1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBpP/P3n2HSVaViR//vt093ZNzZGAYYGDIIEEEEybMWVfdVcG46urqrquuOf7UNa3rumbMcc2rGFEBUUSigEhmGJicQ890Pr8/7u3p291V3VXVqZr5fp6nnqm6de+5p26de29Pnfe8R5IkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkqW5FxJMi4jsRcWdEtEZEKjyeNtH10+iLiJUDvueVE10nSQeHiFhTuPZcMNH1kSRJkiRJkiaLpomugCRJA0VEA/B14HkTXRdJkiRJkiRJkiSVZ9CBJKkevYr+AQe7gBuB1sKyTbUUHBHvAt6Zv7w0pXRuLeXUs4j4MnB+/vIrKaULJq42Gol8tPWX8pf3pJRWTlxtpMHybCR3FxYdkVJaMzG1yUREKrx8RErpkomqiyZOe2pjDbewlQ20s58mpjCb+axgFfNjSc3ldqVO1nArm1lHG/topJGZzOFQjmJJHFpymzvTX7mbv1VU/jwWcXo8vOb6afTUUxvqST2s5252sYO97KSdNjppp4FGpjOTBSzhMFbREtNqrpdG32i3oZ7UzQ62sIsd7GY7u9lBB20AnMpDWBhLKypnU7qP+7iTveyim26mMp3FLGclq2mKKVXXS2Onnq5DvdrSPu7hNraxkTb20UAjM5jNMg5nOUcQETXXS6Ov3trQnrSTXWxjNzvYzQ5a2U0isYRDOSkeVHN9NHY2bu7ig5/YwUUXt7JuYzdzZjVw5gNaeO3L5vKoh06vurxb7+jgez/Zy5+vb+O2OzvZsq2bva09zJvTyCknNPO8Z8ziBc+aRUND6WtJ47I7ht3Hdz6/lGc9aWbVddPYaO/Yw5p1v2frjltp79hDU1MLs2cuZ8Wys5k/56iqy+vp6WLH7rvZtXcdu/euZ/fedXR07gHg1GNfwMJ5Rw+57frN17Fr733s3beR9o69dHbto6GhielT57NgzioOW/YgWppn1fx5Nbrq7T7WK6XEetawiXvZyy466aSZFqYzk3ks5nCOoTEaa67fZGbQgSSpHr288PznwDNSSm0TVRlJkjR57Ek7uZbL6KQDgEaa6KCdrWxgKxtYlU5kZRxbdbltaR/XcCn78xjIRproopMdbMke6UiOjdMGbddEE820lC03AZ20AzCLuVXXS6Ov3tpQJx3cwnUHXgdxYNs97GQPO7mPuzg5nc38WFzjp9ZoGos21MoeruPyEdXrb+ka1uWxgkHQQCP72MMabmET93JGOtfglTpRb9chgG1pEzdwBd10AdDEFHroZhfb2MU2NnMfp6QHH7Q/stebemxDf+Uq9rKr9g+lcXXDze08+lnr2LajB4DZsxrYur2bi369j59dvI//9+YFvOk186oq80c/b+UdH9p+4PW0qUHzlGDz1m5+fel+fn3pfr74jd385OuHMHtW+ZnBF85voLGxdGDC1BaDn+rFntaNXHvzl+ns2gdAY2MLHZ372LrjNrbuuJ1VKx7FyuUPq6rM1v1buO5vX6upPp1d+7nl7p8ceB000NjYQld3G3taN7CndQP3bbqKk1c/l/lzjqxpHxo99XgfA2hP+7meP7CHnUDf/83a2U87+9nBFg7hcBqpPjDr/sCgA0lSXYmIacCJhUUfNuBAkjTWzCRy/9CduvkLf6STDmYxlxM4k5kxh67UyV3czFpu5w5uYlaay4IKRwVDNpLhBv7EflqZynRO5IHMjYV0p27u5Q7u4Ebu4y5mpbksj/4/UB0eqzmc1WXL3pzWcQNXAHAIK2v63Bo99diGGmjgMFYxj0XMYT7NTCUi6Ek9bGcTt3ED+9jDjfyJc9LjmBLNo31YVIWxakNAPrprXv6Yf+DaUYn70p0HAg6O5iQOYxUN0cjOtJWb+DP7aeUG/sSZPKKqOmn01eN1qC3t40b+RDddzGE+x3E6M2MOPamHLaznb1zDdjZzG9dzHKeP9iFRleqxDUF2P5vJ3APXsS2sY1ttSSw1xvbv7+Fp529g244eHnBiC1/55GJOWN3C7j09vPdj2/nYZ3by1g9s4wEntXDeuZV3rB2/upn3v2UBDzt7Kscf08yc2VmQ0pat3Xzp27t5+39s4/I/t/Gv79zCFz5WfhTzlb84jJWHmZ2nnnV3d/KXW79JZ9c+Zs1YxgmrnsnM6Yvp6mrjrvsuYe2GP3LH2t8wa8YhLJi7qqqymxqnMnvmIcyesZzZM5dzw23frmi7hoYmDlt2NvNmHc6cWYfSPGUmEQ309HSxfddd3HbPL9i3fys33va/nPOA1zKlyUDMiVKv97Gu1Mk1XMo+9jKD2RzNScxnCQ3RQHfqppVdbGIdDRy8AZjlw8UkSZoY84FiWPK9E1URSZI0uazjrjw9YhOn8GBmxhwAmmIKx8QpLOIQAO7gpqrK3cJ6dpONyjqFc5gbCwFojEZWxmoOI/uh7E5upif1VFX2Bu4BsiwHvfXVxKnHNjQlmlkdp7I4ltMS0w6kL2+IBhbGMk7lwUCWEWErG2r85BotY9WGZjKHh/MUTouHsSpOYnEsr3jbntTNXdwMwAqO5vBYTUM+Gn1uLORkzgZgF9vYktZXVS+Nvnq8Dt3DbXTROahODdHAkjiUYzg1r/vdtKbdtX1wjZp6bEMAZ/JIHhSP5vg4nUPjSJqZWvNn1Nj63Nd2c899XcycEfz4q8s4YXWWtWv2rAY+/M6FPPVxM0gJ3vr+bVWV++TzZvCm18zj7DOmHQg4AFi0sJE3vnoeb3p1ljnhWz/cS2dnKleMJoF1m6+irX0njQ3NnLL6H5g5PcvG1dQ0lWNWPo5F844DEnes/XVV5c6cvoSHn/lmTjv+AlYd/hgWLzi+4m2nNE1j9crHs3jB8bQ0zyYi6x5taGhi4bxjOPXY5wPQ2bWPrTturapeGl31eh+7g5vygINZnMkjWBjLaMjbUWM0Mjvmc3ScRHOUz3R4f2fQgSSp3gwMVe6akFpIkqRJZyNrAVjKYUwtkSL8cI4BYA87aU17qi53PkuYFXNLlJtlMuigje1srrjcjtR+oJN4GYdXvJ3GzmRrQwDTYyZN+Z/Q7eyvaluNvrFqQxFxIOCkWtvZTEc+jcuKfP9Fs2Me88k6A3rrr4lTj9eh3tHoy1hR8of0ZaxgSj6VkG1o4tVjGwJqvoZp/H3zB1m7eN7TZ7F82eBk2f/2qrkAXHtjO7fe0TFq+z3j1CwQpa0tsX1n96iVq/G3ccsNACxdeDJTW2YPev/w5VnQ7J7WDbTu31pxuRENY3YtmT51Pk2N2TWzvaPya6NGXz3exzpSeyFr2Mk0hdlWSjHoQNKwIqI5Is6LiA9ExK8j4p6IaI2IjojYFBFXRcTHI+LMMdr/uRGReh+F5csj4m35/jdFxP6IuDsivhkRj61yH8dHxGsj4n8j4qaI2BkRnfm/t0fEtyLihRHV300iYlpEvDoifhcRGyKiLSLWRsRvIuKlETF9qM9ZQfnLI+INEXFx/t3si4jdeb2/FhFPjzH+n11EzM4/489K1OGbEfHciPITOxY/O+R37z53F49L/righjquyct/Z2Hxw0uUXdE+RnrcI+Ks/Bzq3d+FFX6O7xe22R8RJxfe6z2G5xc2OX+Iz3huJfscpj5LI+L8iLgwIv4cEVvyz7U3b+c/y4/TghHuZ35EvC4i/hAR6wvn0Y8j4u9qaeMRcXJEfDAirs6vIR0RsTkiromID0XEAyos512FY3pJhdsMeb5HxCX58i8VFh8+xHf5rkr2O0ydGiPiGRHxjYj4W0TsioiuvG2vj4grIuJzkV0L51ZYZlNEPCcivhoRt0TE9ohoj4h1kV0D3xAR82uo66PyMu/K67c1Iq7Pv8+jC+utGe6cjoiVA47lynz5nMiua5fl9e2IiI0R8aOIeMwQZX04Im7Iz4H9EXFnRHwmIqrLF8joHr9S535e/rMj4ieR3T/b8mP5p8jur2WHXPe2eyq7Zpc9N2KUriERcUGZ8+l3ZeqzpkQZw7aXEtssiYg3RsRvI+K+/BjuyM+hCyPiiZWUk5c1qt/RwagrdbKbHQAsoHSKxTksONA5W03H7g625OWWTvM6NaYxg9n5upWXu5G1JBJBsJQVFW+nsTEZ2xBAa9pNF50ATGNGVdtqdI1lGxqJ3v3MZE7JH26hr77b87aqiVGv16G2fN7j6cwquW1EMJ2ZAGwbp3at0uq1DWny2LO3h2tuyALVyk2d8KDTpzJndta19NvLRy/g8Yqrs7KmTwsWLzx405NPdl3d7exuzQKry02dMGfmoTQ1ZkEm23fdNW51G0rr/i10dWdtcFrLvAmuzcGrXu9jm7iPRA9TaC5bL8HgMDVJKoiIJwFfBcrdaRfnjzOA10bED4EXpZR2jXG9ngl8ERgYKrkyfzwvIn4AXJBS+XC3iGgBrgFOKLPKnPyxCngu8N6I+PuU0h8qrOdZwDeBgZMAHZY/Hgm8PiKeXUl5A8puAt4N/AtQ6pejWXm9nw9cExHPSyndXu1+KqjHC4CPAQuHqMPzgLdHxItTSleOdh3G02gd95TSlRHxNuA/8kUvjohfp5TKTkQWEa8CnlFY9PqU0g01fpQRi4gvkgU4lApinALMIGvnjwfeGRFvSCl9uob9PAz4NrBswFu959FTgFflx3rYnMIRMQP4JPDCEnVflD9OA/4tIr4OvCqltLfaek8mEbEa+A5wSom3p+WPZcCDgJcBVwEPHKbMxwL/DRxd4u1D8scjgbdExOtTSl+soJ4zyAIxBl4zpwEL8vq/Li/vf4Yrb4j9nE3W5gb2AC4Bngo8NSLen1J6a2GbVwMfAQYO/ToS+Efggoh4QUrpuxXWYdSP34DyDwe+BXk+5T4tZMfyLOA1EfHElNLV1ZRdRR3G5RoyViLi38gC2WYOeKsFmAscS3Ztv4Ls75Hbqix/wr+jyaaVvj/5Zgz6EzETEUxPM9nNDlqpLP1zR2qjk2wE18wy5fa+18ruisuFvqkVFrLsoE7BWC8mUxtKKdFBGzvYyp15WtGpTGdhnmpUE2Os2tBo1atcnbL3ss7kTtrpSO1ekyZI/V6HshjvRPnxEb3vjVe7Vmn124Y0Wfzt9g5SfqqfsLq55DoNDcHqo6bw5+vaufm2kWU62L+/h7XruvjmD/bwkU/tBOBVL5oz5Gj25758I7ff3cm+/T0sWtDIAx8wlRc9bzZPfLTBl/Wgdd8WyO8JM/JpFQaKaGD6tAXs3ruO1n0TF6SUUg8dna3s2L2GO9f+BoCpzXNYOG/1hNXpYFev97FdbMvfn0Oih7vTrWzkXtpopZEmZjOPQzmKRXFw/3/MoANJw1lJ/4CD3cAdwC6gkawTahW9/wOFpwNHRsTZKaUxye2Zd8R8N99nAm4GNgNLgeMKqz4DWBQRjx2iLlPoH3DQBdwJbAXayD77sUBvaO8KslGTj0kpXTpMPR8I/Br6DQVoA24C9tIXIHEs8FuyTuyKRMRM4PvAeQPeuh1Yn3+uY4HeEbCnA3+MiEeNZid1RLwF+H8DFm8GbsvrcDx9n/944LcR8YyU0i8HbLMd6F02DXhY4b3LYFCe2HU1VPdSsg7DVcBR+bIdwJ/LrD9oH2Nw3D9M1mnYm5njsxHx55TSoBDfiDgJ+Ghh0Q9TSp8asFrvMTwJDvzivB64scS+gXwSq9qdTP/OwrX5/vaSdRYeQ9YxRv76UxExN6X0gSr38R04MNnj7WTfzTzgRLLrEMDDgd9ExMNTSmWHZ0U2Qv+XDO4w/xuwiSzg4Hiy60sALwCOi4jzUko7qqj3SP2Z7HqxnOxzkr8ud925o9YdRcRC4BLoF6a7H7gV2EbWrueRfZ+9vz4PmS0rIl5J1mFeHJqwjez72w8cSl9n+lzgwohYllIaeD0pljkVuIjsuy7qbRNzyI5VC/DJiOgcqo5DOJEs4GAG0EN2j9lCFmBXvF+8JSI2pJQ+mXc+fzhf3nud30N2rekNXGgBvhkRt6eUrh+qAmNx/AZYDHylULe1wBqy7/rk/LP3rvfLiDghpbRxQBl3kJ1LlVyzAUpdA0fzGrKOvmtgMdvRVZS+1m0qsaxiEfFZ4OUDFt9H9jfEDLJ21HvdOhv4Q/73yLUV7mI0vqODTjttB563DDFHcAvTgB391q+83NIjhIED8xJXWu7etIs97AScWqFeTIY2dHO6mvWsGbR8JnM5mbNoLJ9cTONgrNrQSPXuZ/g6ZTpoo3lQHKXGQ71eh6YynX3sKfvDfk/qYT9ZnHY3XXSlLprCn50nQr22IU0eGzb1zXJ6yNLy5/GyJU1Ae7/1q9F86B10D5hBoakJXnH+HN7370Mny7zq+nZmzQymNAXrNnTzww2t/PBnrTzryTP52ieX0NzsVB4Tqb2zr9O4pbl0hhyAlimzgXW0d47/OJ+b7/wR6zcP/u/5zOlLOfmY59DYaOr8iVKv97F9+d85jTRxNZeym+0EQSNNdNLBNjaxjU0cllaxOk6tqE73R/71J6kS15H98H1RSmlQx1ZELAX+GXgD2XXlFLKdZBkXAACs1ElEQVSO6H8do/p8jawz8MfAa1NK9xTqchRZR83j80UPBd7P0B3628myOfwY+GNKqV+IbmRTKjwN+BBZkMAUso6jVeWCGSKbMuFb9HW4d5GNjv94ccR0RJwOfIYsU8R/DvO5iz5PX8d3N1mmgY+nlNYXym4gGwH+P2Qd0AuB70bEaSml1ir2VVJEPJ7+AQf3Af8E/DSl1JOvMxV4Kdlo/un549sRcVJK6b7eDfMO+cfl26ykf7ru81NKa0Za35TS+Xn576JvioUbUkqPq6KYUT3uKaUUES8E/kLW4Tub7Pg8OKXUWShzOv073tcCLynxGXuP4Zfpm2Lh1ymlC6r4jNXoIOuc/R5wcakMJ/mI8Q/S1yn53oj4ZRUdb18g+9yXA69MKd1UKHspWdt6Yb7oOOCz9M8GMdBn6B9w8H/A61JKB9pcRKwg+26fmS86A/gcg0fXj5mU0hvzulxA3xQLm6psr5V6K30BB3uB1wHfSCn1++s6silSHkh2XEplROhd7/Fk7b/3f/mXA28BLk8ppcJ6R5Md5yfli94bEVellH5Vpuh30z/g4DfAq1NKtxTKnAe8jez+819k195qfZmsQ/eLwNuK2TMi4gSywKPekPd3RMStZO2wPd/3p1JK+wrbPA34Btn1r4kseOhR5XY+hsev6H/Irk2XAv+SUrquUO7U/HP0ZnGYD7yXLMPFASmlrwNfH+E1e9SuISmlX5MF+hH9p1h4Y0rpkgrrU5E8KKQYcHAz2fXpssI6s4HXkx3HRrLj/b2IOGWoDEwFI/6ODkY9hVO+gfIdr435e90VXiK6Ky63qapyezuOp9DMwkHJfDQRJkMbamIKzbTQQ8+BKRVmMpdjOZXpUf5HXY2PsWpDI9Vbr0rqBNA1TvXSYPV6HVrAEvaxh43cy5HphEHTdKzn7gOjB7PtO2nyZ+cJUa9tSJNH676+/05Nm1q+8376tOy9va09Ne1n6eJGurpg954e9rdl+3zF+XN406vnMWVK6f2+8O9m8dynzeSs06Yyd07WDm+5vYMPf2oHX/72Hr73k73Mnd3AZz9SenS9xkdPd98YkIaG8veC3o797u72Ma/TQE2NU2meMpOenu4DUyrMnL6UY494ItOnjWiGWI1Qvd7Hev/vtZXsZ8IjOI7DOYammEJHauN2bmIDa7iXO5id5rEsDs6BBUOOUpMk4MsppdNSSv9VKuAAIKW0MaX0FrIRwb1eHhXO+V2DRWQdsE8vBhzkdbkTeDLw08Lif46I48uUtQ84LKX0LymlSwYGHORldubpsM8i6+yFrDP5+UPU8fX0n1LhRSml9w1M0Z5SugZ4BHBt/rmGFRHPIZvqAaATeEpK6Y3Fju+87J6U0o/I0qH3jug8BnhVJfsZpg5NZJ23vTYCD00p/V9vwEFeh7aU0ifJgjZ645fnUl2ARV0Yq+OeUtpMdu70HrczyQJlij5BXxaPbuAfxnnUfTnnpZSel1L6frkpVVJKV5B1sF6UL2okOz8qtQj4PfDoYsBBXvbGPJikmG796Xk2lEEi4jzgOYVF3wSeVgw4yMtdSxZg8NXC4mdFFXOzTzJPLjz/15TShQMDDgBSSt0ppStSSv9GHiQ0UJ4N5Cv0dZh/FTg3pfT7Yod5Xt7tZAE63+jdnKwTvVS5K+nfbn4JPK4YcJCXuSOl9HqyTvqpDE57X4kFwAdTSi9JA6brSCn9lex61vs/j0Vk95sguyd9pBhwkG/zI/oHvj0iT5s/yFgdvxIWAj8hO6+uK76RX7ffBlxYWPzciDITQI/MeFxDRlUe2PLhwqKbgYcUAw4AUkq7U0rvpH9wwhHAOyrc1Zh+RxFxTblHpWVoZFJKbOReAJaygobwv+aqzDFxCg+LJ3NuPJVzeSonchZddHA1l3Bb+stEV0/S/dQKjqaRJnro5jp+z/a0me7UTUdq5950J7dxA0Gxk9BRxpKGtvbaI1h/wxHsuetI7r7qcP71FXP5zFd2ccoj13LpH0snrP3Sfy3hsY+YcSDgAODYo5u58D+X8G+vmgvAhd/cza13jGzKB93/HbPycTzsjDdy7gPfzLlnvoUTj342Xd1tXP3XC7ltzS8munqqS30/yy1lBUfFCTRFFjjTHFM5Ic5gdp4wfA23lCzhYOAvG5KGNLCTfJh1vw38MX85g/7pjUfTDrI51ktOJphS6iYb7ddb9wbgFWXW7RnYQVRO3jn8vsKikqOp89HAxU6Gi/IRoeXK3UuWDaBS/154/sGU0s+GWjmldC9ZFoper6liX+U8nf5znb92qJGt+QjU4lQAT89Hk08mY3bcU0oXk42U7vX6iOjNWvAc+mc1eHdK6fKKaz2GKr0+pJS66N/p+pT8PKlEJ/DSlNJQYc+vh7znJvNPZdZ7beH5JrKRyeWuIykvpxhU8rphazs5HVZ4/vtKNsivs6W8lL4AqjuBlw+xbu9xfhXk+cXhhIh4ZIlVX07fVANtwMvydlXOf5Bl6anFbcDby72ZBzr8rrCoGfhSSunnQ5T5VfruSQE8uMx6Y3X8BtoDXDDMMfxQ4flM4AEVlFuVcbqGjLaX0je1AcCLhwoCSyl9kSx44MD2EVHJRKN18R1NNg2FEZU9lD116M7fa6xwBGZjxeV2VVzuNjbSkadrdGqF+jGZ2hBAU0xhaRzGGTyCRppYy+1sTrXMQqbRMlZtaKR661VJnQBHqE+ger0OTYsZnMRZNNJIK7u5lsv4HT/kMn7CrVxHMy0czjEH1p+CaaknSr22IU0eM6b3BQ31ZiAoZd/+7L2ZM0bWxRQRrDh0Ch9+50I++q6FbN/Rw/P/aSP79lWXQeEdr5/PtKlBSnDRxSNO8KoRaChMTdDTU/6/tN15RoTGxomd0qmpaSpLF57EGSe8lMbGFtZu+CObt908oXU6mNXrfaz4egWrSm67Ip8JtZU9tI/NzON1z6ADSaPtisLzgXOmj5ZvpJSGnIs+ZfMaf7ew6Jnl1q1SJZ/vFLL5tnt9usx6B+SjGP8w3HoRcSpwav6ykyx9eCX+Fw5MRHRYRBwz1MoVeHrh+Vr6H+tyPkZfSGAj2QjdSWGcjvs76AvaCeArEXEOWVr/XpfQf0qLSSMflb0tfzkTKJd9ZKBfppRuG6bs/WTTMPR6fD4lxQH56/MKiz6fUio9IWlfuXvJpmvo9cg8Zfr9TfGv4JF2Wl5QeP6JYYJFgGxEOPDDwqJHl1jtSYXnP82DeoYqs4f+gU7V+OIwHb0Afxrw+nMl1+qrTxtwfWFRufZ/QeH5aB6/gb5dwX30NrIsNr1OqKDcMTOCa8hoK97/Lk8pXVnBNh8pPJ9LluFoOGP6HaWUTi/3qLSMelSc73GoeR3b88veUPND9i+3L4lEO+V/OOioYM70XhvIknXNYDazY15F9dDYm0xtqGhqTGMxy4G+aTs0McaqDY1USwVzrBfbZvM41UuD1fN1aGEs40GcxwqOZhZzaWEas5jLSo7lLB5N5D8ztzCNhgmLD1U9tyFNDocs7etYW7+x/H+NN2zK3lu2ZPQCTF72/Dm0tATrN3bz899WNEbsgBnTGzjx2GYA7rrH6T0mUktz309n7R3lZxds78x+lmuZUkuSytE3tWU2i+dniWbXb6l0VliNtnq9jxVfTz8wo3Z/xeVtQ+zj/syQQ0kVi4hFwGPIOtUPIZt/fmAoYjHM61DGxlCjSYsuAl6UPz8kIg5NKd1XbuV8yoBzgdPJ0uHPIRvNWMwLWExdPD8ipuUdnkVnFZ730H9E7FB+S/nRr72K85lfl1LaVnbNgpRSe0TcQl/H+Rlko3lrdXbh+UXlRosPqMOaiPhLoQ5nA58cQR3G05gf95RSV0T8PVnH5FxgMdmo894Awa1k0yrUNlneGIuIB5C13+PJ5hefBYMmyCqO7j0UuLGCoqs539+dP28CTgOKGSHOpP/fPcWRx0P5caHcBrLz+9cVbjtZXEVfR/UnI6IT+OFQI+xLyafUOamwqJrjVMwHfcaAcmfSvzP1NxWW+dsq9l90xfCr9Ovo7QAqSUlfnKphUA/jWB2/MoYNcsvdByzNn8+toj5VG8NryKiJiGaya0uvSq8jvyfL0tT7vZ9N/2mgSqm772gymFH4D34ru/u97pVSYl+eeGQGlcWRNUcLU1IznXSwl90sOHDI+9vL7orK7UwdbMkT6RxiloO6MlnaUCm9P6LtP5BYRxNhrNrQSM1kNtvYSCvlY25byToFptBCc0zsiMODWb1fh6bFDI7hlJLv7Uk7AZiLc2FPpHpvQ6p/x65qJgJSgr/e2sHqVc2D1unpSdx6ZzZK/fhjBr9fq5aWYMG8BtZv7OauezpHrVyNrxnTFpL9nJ5o3bc5f91fSj3s25/9vDpj+uLxreAQegMm9rfVw6y2B6d6vY/NYA5b+/0cqFIMOpA0rHzu6Q+Tje6r5roxd0wqVHknw8D1jiH7cb6fiJhCljb9DfSlta7UXBgUtlb89XhtpdM3kM0LPZyTi/uJiGommSrWq9rPeUAenLGysKiayWNvoK8DvnQeovo0Lsc9pXRPRLwE+H6+qJiR6IKU0voSm02oiHgK8EHguCo3nVvhepWe738ly6TRGyR0DP2DDortLZG1xUrcDHTRd+1bxf0v6ODDwKPIjt18sswlW/J2/nuyUf03VRBcdBL92+wnIqLSwIXlhecDz5NDB5T7twrLvJss00i1Q2wq+R9E8bq+rYLMCAO3mV7i/bE6fqVU+r+kYk7KUnUesXG4hoymw+gfbFnR/S+llCLiRuBh+aJK7n918x1NJk0xhdlpHrvZwTY2HRj5XbSL7XSR/YA5n8p/3JrHYjZzH9vZ1C99dK+2tP9AZ95w5W7iXnroIQiWGnRQVyZLGyplf345MJ31xBrLNjQS81jEPdzGXnbRnvbTEtMGrbONTeNaJ5U2Wa9DHamd7XkbWtJvJkaNt8nahlQ/Zs1s4IxTWrjq+nYuvmwfz3ji4FHoV17bxq7d2ZiYRz5k8D2lVntbe9iyLftvcLXTNrTu6+GmWzoAOGKFfw9NpKbGFmbPOITdrevYtutOFi8YnKhw19776OrORpTPn3PkeFexrN5gg8aG0QumUXXq9T42n8Xcw60A7GMPs5k/aPt99GX2mHaQ/kTj1VfSkCLiTOBX1Pbj/lgNj6holHmJ9UqNLJ0G/B+VpaMupdRnnFt4vrOKsioJoSwOGVgCPLaK8ovm1LgdDG4LW6rYtrjuZMolPG7HPaX0g4i4hCzrRq8LU0oX1bjPMRMR7wPeWuPmlV4fKs0q0RYRrWRp12Fw+yq+3punu6+k3M6I2EVfG5hM7bYiKaVfRcSrgY/DgclXFwEvyB8A2yLiZ8CXU0rlMggMHNJU63V14Hkyd8DrnZUUknf07oQyocvldYzx+tA/g06vsTp+pQw7bUMJpeo8IuN0DRlNA8//sbz/1cV3NBktZQW72cFG1nJkOm5Qx9o9ecKhWcxjRpROiVi63MPYzH1sYxN70k5mxdx+76/Ny21mKvOG+dGjd2qF+SyhJUw9XG/qsQ31pB4aovwP7/vSngPZM+YyeCSZxtdYtaGRmM9immmhg3bu4bZBI9X3pJ0HOoyXcti41Enl1eN1aCgpJW7jenroYSZzWMSyirfV2JhsbUj153lPn8VV17fzzR/s4e3/On/QFAof/fROAE4/uaVkJoRyuroSTU3l/9vyic/vpDNPcPCQs/r/nZxSIqL8tu/7z+3sb0tEwOMfOaPsehofSxedxO7WdWzcegNHHnouLc39rzX3rM9mmJ0145CSmRDGQk/qHnL6n337t7Flxy0AzJ1tcPhEqsf72DwW0cI02tnPWu7gxBIzb6/ldgBmM4/mg/T/+tWFi0k6qETEDOAH9HX4dAJfB55LNiJzPjA1pRS9D/rSkI+lSjt4Bv5gX6qD4v3079i5FngtWerjQ8hSOTcWPt8RVdZ1tI3WX80juf4PPI7VdLgVv5PJdOcdt+MeEefRfzoHgMdE1NeEzxHxVPp3Fq4D3kN2Ph1Jlhq9acD14Z4adlVr+xrYTouvq+0knqzttmIppU+RTWHwGaDUPPILyAIQfhMRv42IUtPn1MP1aTI7qI7fOF5DRtPBeP+bdJZzJFOZTjddXM8f2JuyUQpdqZPb0w1sYR0Aq/rN2pK5OH2Pi9P3uDP9ddB7izjkwEiGG7iCXflMSz2pm3vSbQd+XDiK44fsHG5Ne9iVX2adWqE+1WMbuo3ruTVdz860le7C7EedqYP1aQ1Xcyk9dNNIEys4ehSOgkZirNoQZN95R2o/8OjVTWe/5T0DZmRriEaOJBtluJbbuSfdRk/elnambdyQzy41hwUsikNGeAQ0UvV4HQK4I93ItrSRrtSX8nx32sFf+CMbuZcGGjmeM4bsFNT4qNc21J26+l+ryK5VPfT0W95VUSI5jaWXv2A2hx/axJ69iae8YAM335r912fP3h7e9N6t/PBnWYal97158HQqjcvuoHHZHbz7I4PHkJz48LV88sKd3Lmmk2IyxVvv6OB1b9vCOz6U/Z38tMfP4KTj+v/36zkv38jbPrCNq69vo6Oj/7Yvf/1mPvTJnQC88O9mcfxqR6lPtOWLz2Rqy1y6u9u5/pavs3ffZgC6utu5/Z5fsmV7lvB31YrB4y0uvuIdXHzFO7jz3tJjXjq79tPR2Xrg0au7u73f8p6e/skjb7v7Z9x690Xs3LOW7p7OfuWt33wdV//1i/T0dNLY2MKKZWejiVOP97GGaODofFbUjazlzvTXA38TdaQ2bk5XszsfU9r7d/fByEwHkobyIrK01pAFHDwmpXTpMNuMx1CNWVSWFWDghD67ii/yTtx/Kiz6LPDKYVKIV/L5dhaez61g/V6VdCoXy/6/lNJTqyh/tOwc8Lqa77z4nQwsp57tLDwfs+MeEUuArzJ4tOoK4PPAs8ZivzV6e+H5VWTXh13lVs7Vcn2otX0NrMvOEdRjLNpt+dDqCZJSuh14ZUT8E9k0KA8mSwf/CPqPwn8E8LuIOD2lVJwYeOeAIuenlEZjEryB5c6tYttq1p1oOwe8Hq3jV6/G6xoymnYOeH0w3P8mncZo5JR0DtdyGXvYyZ/4FY2piW76frxexYksiOqSoEQEJ6cHcQ2Xsp9WruJ3NKYmeugmkf3puJwjWR5DpwbtzXLQxBQWYcdeParHNtRNNxu4i3u5A4CmlCUm6k0pCtlonJN5EFPj4EzjWU/Gqg0BXMnFtPWbsSlzI1f2e30aDxuUEvbQOIo9aSfruJvbuYE7uJGG1HigXtOYwck8qOo6afTV43UIYCP3siZPK9yYmkj0HOg0nkILJ3EWs+srVv6gVa9taA23cneJ2fK2sP5Axh6AZRzOCZxZVd00uqZNa+CHX17GY569jmtvbOekc9cye1YDe1t76OmBCPh/b17AeedW93fH7Xd18tq3beW1b9tKS0swa0bQui+xv63vp9jHPXI6X/nvJYO23bqtm+//dAcf+MQOGhthzuwG2tsTrfv6tn3mk2bw6f8wy0Y9aGycwimr/55rb/4ye1o38Ke/fJLGxha6uzvonR111YpHsWBu9bPvXnnDp2lr3zlo+Y23/2+/16cd/yLmz+kbP9jd08mGLddz78YrgaCpcSqQDkzzANA8ZSYnH/McpraMJEmwRqpe72NLYwV7064D97M13EJTmkJnYUzK0ZzMwjh4sz4ZdCBpKI8rPP9WBQEHwLjkYjyCyoIOBt4dNg14/Sj6UonvA15fwZzllXy+4kjMFRExPaU0+JehwSoJgSvO8Tz4L/BxkFJqjYh99M0dfVQVmxfX3Tx6tRpzY37cIxsO8rVC+buA/wHekr9+ZkT8Y0rps2Ox/2pExCLg9MKiNw3XWRgRM6mtA/gI4LoK6nQofeczDD7fi+2tOSIOTSndV0G5i+mbsmFgOb2Ko52nlHi/lLr9NS6l1EOW9eVa4L8johF4JPAu4Jx8tVXAq8myxfQaOAf9Eiq7Vg/nPqCHvhH8xwHD3o8i4kgm14jysTp+dWecryGjaeD5fxTkQ0OHN1nvf5PSrJjLg9J5rOEWtrKBdvYzhRbmMI8VHM38qO1WPjWmc1Z6NGu4lc2so41WGmliFnM5lKNYUjIJTJ+U0oGggyUcNmRqT02semtDK1nNDGaxnS3sZy8dtNFDopkWZjKHBSxlOUfQFJX+GaKxNlZtaKSOi9OZnxZzH3exh5300M10ZrGY5axktW2ojtTbdQjgCI5jKxvYw046aKOBRmYwm0UcwmGsYko4srie1GMb0uRyygkt3HDJCj74iR1cdHEr6zZ2s2BeI2c+oIXXvXwuj3po9YGOP/rKMn77+3388ao21m/qYsu2bqY0BauOmMKZp7bw98+cxRMeVToJ4L//83xOOr6VK69p474NXWzf2UNDwBErmjjr9Kmc/3ezqw6C0NiaNWMpDzrln1iz7vds3XEr7R17mNI0nTkzl7PikLOZP6ean5RHbuXyhzJj2iK277qL/W3bs2wIqZvmKTOZOX0xC+Yew/LFp9HUNJl+Srr/qtf72Ko4iXlpEfdyJ7vZTicd+XQMC1nB0cyJwRlgDiYGHUgaSjHn65+HWznvND1nuPVGwVlknWGVrNerA7hxwPvFz3dzSqmV4T2kgnWKw0wayEYFX1TBdo+sYJ0/knX0AZwaEdNSSvsr2G60XQM8NH9e0XceEU3Qb7Kjq0e7UhUq5hqtNO/jeBz3NwKPKbx+eUrpfyPiOODp+bL/jIjLUyqTb7VPLZ+xGisGvB72+kDWTmpJ+34W2TQvlaxXdM0wr88B/pfhDWzfpdptcbT//ArKBPJ8XMMb6+9yWCmlbuDXEfEHsgCQY/K3Hkv/oIMbgFb6pgk4G7hlFPa/NyL+St8xexTZNBDDqeSaWk/G5PiNg54Brytpp2N9DUmFeozaeZNS2hIR99IXgHgO2bRTQ4qIBcDqwqKJuv8dVFpiKqs5ldWcWvE2j47hEwo1xRRWcSKrOLHqOkUED+WJVW+niVFPbWhGzGYGs1nJsRVvo4k3Fm3oIfGEEdYKlsRhLBmXsQIaqXq6DgEsjyNYPuEzTqoa9daGjooTOKpEKmzVr6WLm/j4+xbx8fctqnib7g3lR64/+bwZPPm82mYWPO/c6QYVTEItzbNYfcQTWH1E5X/DPPrs9wz5/kNO+9ea6jJj2iJmLF/EyuUPHX5l1YV6u4/1WhBLWUD1WcsOBpNizldJE6baYQ6PA5aPRUUGeF6F6/194fmVKaX2Ae9X9fkiYgrwwgpW/QvZyNxer6yg7AeQpTIfzsVwII9qC/D8CrYZC8VRxo+OqCiX0RPpn6K9kswZY6EYXDKtwm3G9LhHxFnA+wqLLkwp9XaIvwRYmz+fBnw7IoYLua3lM1ajliFQL6lxX3+Xj7Qfzj8Unt+bUrq7+GZK6S76n5cvqHD/5xeeb4I8n2h/xewmqyIqymv89OFXAcb+u6xYnrHlF4VFSwe830l2rvR66Sju/qeF50/KM1uUFRENwKtGcf9jboyP31gaGLBXSTsd62vIWJ43xXvXsyq4HkN2zyj+v+uy0a2SJEmSJEmSNLEMOpA0lPWF5w8basW8k+0/x7Y6Bzw0IoYcJhYRzwFOKyy6sMRqxc93UsSwkw++nQqCKvJRwZ8rLHpiRJTtpI6IGcDnhys3L3sL8JXCovdFxMARo+PhQvpGt04B/mOolSOiBfhAYdE9wK/GpmrD2lB4flSeoWNIY3ncI2IO8C36sg/dAvxzYd87yDrUu/NFJzL8uVb8jEePRj0HWD/g9XDXh0cBz65xXyuBVwxT/tnA0wqLSp3v0P88e1JEPGKYch82oNzPl5mCpZhFoQl4xjDlPp/KMx0Uv8tFeXsZNZW0/wGKU01sL/H+hwvPz4mIIb+7KnyOvnNgKvD5PHtKOW8EHjBK+x5PY3X8xtJOoK3wupJrzlhfQ8byGli8jiwC3jzUynmWg7cWFl2eUrp5lOskSZIkSZIkTSiDDiQN5beF58+KiCeVWin/Qf2n9E8dPNa+FhFnlqnPQ4EvFBbdAXynxKqXkKVghmz0+idLjaiOzL8Ab6uifh8D7iq8/lJEvDWfk7pY9mnA78jmtt5SYdnvBrbmzxcDl0bEg4bbKCKWRMSbIuIbFe6nrJTSGvp3wr8wIt5eqgMzD6r4Dtk87L3emwdnTIRiB/F84EUVbjdWx/1zcCBHZTvw3HxE+QEppcuBYm6xV0TEUB3bxc94SkQ8erh6ViOltBa4s7DoI/l1YJCIOBf4PiNLcf7RcoFG+fQTxfK3A58uU86n6H+efTciTi+1Yp595HuFRduA/y61bkppPdkUHL3eHxElA5Qi4glUNjVArxvoy7IBUFsOufIOj4g/RMQzI4aehDW/5j63sOh3A9dJKf0B+HZh0Sfz9j/klF4RMSUinhIRv4uIwwe+n19zPlpY9Djg5xHRL890RMyLiI+QBTm1AXuH2m+9GavjN5bya/n1hUWvGm70/zhcQ4rXwBdXEFRYsZTSZfRv+2+LiJL3kYhYSPb3UW8u0kR2L5EkSZIkSZLuV4b8AVPSQe9zwJvIRrY2AD+OiK8BPyFLMz4PeCjwYrK0+buBi6h8+oNafSvfxx/z+lxE1pG4BHgS2ajw3uCBLuAlKaW2gYWklNZGxHeBv8sX/T1wXER8Dvgb2Qj+48jSsPcGOHyGYUZd52W3RsTzyFJlzyK73r6PrHPiRrLUz4fT19m8BfgX+uaG7hii7Psi4lnAL8mCJVYCV0TEb4Gf5XXfRTYv+CKyEdUPpm8+7NGa1uB1wMOBI/PX7yEbPf5lshT0U8iyTbw8r2OvH6WUyo1EH3MppVsj4mrgjHzRhRHxZuB2+h/3T6SUflvYbtSPe0S8lL72B/CGlNJfylT9fWRz1D88f/2FiLg677wb6LdkI32XkXXU/ToibiKbpqHYgf22lNJNZfY3nI+SdeIDHA/cGBGfAq4kO46HA08lm0YgyI7RiQyey304vef7TyPie8APyaZJmA88huz6U+zgfE1KaXOpglJKWyPiJcCP8zotAP6UX0d+Bmwm++4eRzatQm8K+AS8rFy5uQ+QXRshm+/9uoj4BNnx6CQ7159Bdo0C+DJwwXAfPqW0NyJ+DPROSPaOiHgxcDOwv7Dqt1NK3x5UQGXOyR+7IuIXwJ/JzoedZNfSw4BHkwUc9AYmbKdMEAZZGvxjyM7/RuCDZB3R38nL3kJ2/Ofm650BnAf0ZnEo17n8TuBB9I2KfzTwt4i4lWzk/Byy8673e3st8Bb6sjMMnGKnXo3V8RtLXyf7bsjrsiEirif7u6A3uO+mlFIxeG8sryFfpy9A5mTgvoi4lqzd9tZnc0rp5VV8xqILgOvIrkMNwBfze/63gLuB6WTn1MvpCzgA+K+U0sVIkiRJkiRJ9zMGHUgqK6W0OSLOJxul3kT2w/r59J/jvFcr2Q/8Z41D1f6RrKPldLJR6uVGqncDz89HJZbzKrIU3L3plx9A+VHSXySbRqCidNcppT9HxHnAN+kLLphKXwBDr1vJOhSLc5TvGqbsS/OMDj8obPfI/DEuUkq7I+LhZHO8n5AvfmD+KOcHZMEdE+1lZAEhvSNrV+WPoh8N3Gg0j3s+Qv+/Cot+klIq14lLSqknIv4B+Ete73nANyLi3IFZI1JKnRFxAVkH/fR88Yn5o+jj1da74DPAo4Bn5q+XAe8ts+61ZMFA19ewn7eTdaY+gew8edYQ674hpfTNoQpLKf0k7xz8KlkHehNDX0c6gQtSSj8cptyfRsQngVfnixZR/ni8iywI5YKhyiz4F7KO5ZX560Ppf72A2o7tQHOA5+SPoewAnpJS2lDqzZTSvvza8GX62scK4A0jqVxKqS3PFPFl+reD1fTPtNMBvD6l9LmIKGYIGfK6Wi/G6viNsc8CTwYem7+eC5w7YJ25A16P2TUkpXRRRFxIFsAB2XXwIQNWu6eSssqUv7Zw/+vNavKY/FHOJ4HX17pPSZIkSZIkqZ45vYKkIaWUfkA2mrTcaOhu4FfAaSmln49TnfaQjSD/L7Jgh1KuBM5KKZWaVqFY1jay0ZnfoG++8IHuIut0fEmZ94cq/09kHfL/TNbJuJmsQ+w+stHoLyc7djeRZWroNexUCymlq8gyMbyJbAT7ULqAK/J1R63TP6V0H1kQxdsYus63kXUYPSulNOGjjVNK15N9L+8Efk9W97LZJQZsO+Ljnqce/zZ9AQHrqWCah5TSOrKR/b0ekn+GUuv+imzU94fIzodt9M9yMCIppUTWOf0OstHMpewgG6V9dkppZ4276ibrzHw72SjlUm4BzkspfaSSAvPrwslkwSPljkkXWUaEU4YLZCiU+xqyDCA7y6xyG1lnfVXp1fPz7BSyDsvfkGWxGJS9pUabyDqzf1dBmbvJOpePz6cBKCultDel9Czg8WRT2Qw3ncoasoCvh+RTKZQrtzWl9Gyy+9LX8+3ayNrGDWTt/cSU0ifzKQnmFzavdAqbCTdWx2+spJS6yAKD/p4s2OlusvtzGmKbMb2GpJReSpZZ5Ftk596eoepTrfy+fRJZxoZy9YdsqocnpJRek1LqGa39S5IkSZIkSfUkst/7JGloERFkqZ7PIBtlvYes4+vylNLGMd73uRTmT04pReG9GWSjzFeQTWOwCfhjSunWGvazjCx1/WH5oo3A31JKV9da9yr3/ynglfnLb6SUnl/l9keTfT8LyUYs7yfraL4NuDEP1hgzEdFAFoBwAtko7y6yIIurUkq3jOW+J9JEH/d6EBEzyVLeHwNMI+vcXQNcmlIatUCHiGgmGz19BFln8hbg2pTStSMoczZ95/1cstHw95LVvaaR8RHRkpe5miy1/yay1PJ/rrWe4yEippB1oh5NNup8Jlkwznay6RyuLTVVTYVlzyELFjuU7B6SyI71GuDmlFLNo86H2OcDyQJuILsezU4p7R9ik7o1EcdvPI3XNWSs5OfOg8nOnYVk94FNwB/KTIFT1x7T8Gz/gyhJkiSNwC/XXz/RVdAk99hnvHCiq6DJ7k83THQNNMn9uue7VU/hatCBpLo3VNDB/UXe4bKWLGU+wCtTSp+ZwCpJ0qQWEZ8HXpq/vDKl9KCJrI80WRh0IEmSJI2MQQcaKYMONGIGHWiEagk6cHoFSRojeXaIStZrBD5HX8DBPrLU+5Kkgiquq0+i/1QkF45NjSRJkiRJkiRJBh1I0tg5LSJ+HxEXRMTigW9GRENEPAz4LfC8wlsfrXbuakk6SPwyIt4TEaeUCkCIiEMj4oPAj+j7O/c24OvjWEdJkiRJkiRJOqg0TXQFJOl+LICH5A8i4l6yueL3AbPJ5nufM2Cb3wHvGcc6StJkcgjw9vyxJyJuA3YCLcBy4IgB6+8CnpdS2j+elZQkSZIkSZKkg4lBB5I0dnoGvD4sf5TSTTbFwutSSl1jWitJmryK19VZwOlDrHsd8A8ppb+NbZUkSZIkSZIk6eBm0IEkjZGU0rURcQLwROBs4FiyUbozgE5gO3A7cCnwtZTSnRNVV0maJB4GPCn/9xTgcGAu0EiW8WAD8EfgJymliyamipIkSZIkSZJ0cDHoQFLdSyldQjZVwaSTUroZuHmi6yFJ9wcppZ3A1/OHJEmSJEmSJKkONEx0BSRJkiRJkiRJkiRJ0uRk0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSapJ00RXQJIkSZIkSZIk6f7giQ9+6kRXQZPcL//w1Ymugia5xx5y6kRXQQchMx1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSJEmSJEmSJEmSJKkmBh1IkiRJkiRJkiRJkqSaGHQgSZIkSZIkSZIkSZJqYtCBJEmSJEmSJEmSJEmqiUEHkiRJkiRJkiRJkiSpJgYdSJIkSZIkSZIkSZKkmhh0IEmSJEmSJEmSJEmSamLQgSRJkiRJkiRJkiRJqolBB5IkSZIkSZIkSZIkqSYGHUiSJEmSJEmSJEmSpJoYdCBJkiRJkiRJkiRJkmpi0IEkSZIkSZIkSZIkSaqJQQeSpLoSEXMi4g0RcUlEbI6IjohI+WPnRNevWhHx5UL9v3yw7b9aEXFBob5rJro+GrmIaIiIf4iI/4uItRGxv/Adp4g4daLreH8RESsHHNuVo1j2ucWyR6tcSZIkSZIkSZNf00RXQJKkXhFxNPAb4LCJroukkYuIacBFwCMmui6SJEmSJEmSpLFh0IEkqZ58k/4BB3cCa4Cu/PXe8a6QNJyIeBfwzvzlpSmlcyeuNnXnvfQPONgC/A3YX1i2e1xrNIryTAJ3FxYdkVJaMzG10cEgIi4BHp6/fHdK6V0TV5v61p7aWMMtbGUD7eyniSnMZj4rWMX8WFJzuV2pkzXcymbW0cY+GmlkJnM4lKNYEodWVdbadDu38RcApjKdh8QTaq6XRl89tqF9aQ9ruYPtbKaNfSQSLUxlDvM5lKOYF4tqrpdGXz21oTvTX7mbv1VU/jwWcXo8fPgVNebqqQ2V472svtVjG/JeNrm0d7Vy184r2bzvLtq799LU0MKclqWsnHMaC6YdXnV5PamLbfvvY3f7Rnblj/buVgBOX/oMFk0/Yohte9i2/x627LuLnW0b2Ne5g+7URXPjNGa3LOHQWSeyZMbRNX9WjY2Nm7v44Cd2cNHFrazb2M2cWQ2c+YAWXvuyuTzqodOrLu/WOzr43k/28ufr27jtzk62bOtmb2sP8+Y0csoJzTzvGbN4wbNm0dAQg7Zta+vhoov38Yvf7eOq69q4655OOrsSSxY28aAzpvKK82dz7jnV10ljp57uYz2ph/XczS52sJedtNNGJ+000Mh0ZrKAJRzGKlpiWs31uj8w6ECSVBci4jTgjMKi81NKX52o+kgamYhoAF5SWPQF4BUppe4JqpKkg8SetJNruYxOOgBopIkO2tnKBraygVXpRFbGsVWX25b2cQ2Xsp/WA+V20ckOtmSPdCTHxmkVl3Unf626Dhof9diGNqd13MSV9NADQNBAA0Eb+2hjH5u4jyPSsRwVJ9b4qTWa6q0NNdFEMy1ly01AJ+0AzGJu1fXS6Ku3NlSuLO9l9ase25D3ssllT/sW/rzhf+nsaQOgKZrp6N7Pln13sWXfXRwz/yEcOfesqsrc27GdazZ+v6b63Lz1Yu7bc+OB10EDDdFEe3frgTotmXEMpyx+Ag3RWNM+NLpuuLmdRz9rHdt2ZOf87FkNbN3ezUW/3sfPLt7H/3vzAt70mnlVlfmjn7fyjg9tP/B62tSgeUqweWs3v750P7++dD9f/MZufvL1Q5g9q//s8k85fwO/uaxvDExLSzClKbh3fRf3/t9evvt/e/nnl87hP99r8FM9qLf7WCcd3MJ1B14HcWDbPexkDzu5j7s4OZ3N/Fhc46ee/Aw6kCTViwcWnq+9vwQcpJQuAC6Y4GpIE+EY6Per9fsNOJA01rpTN3/hj3TSwSzmcgJnMjPm0JU6uYubWcvt3MFNzEpzWRBLKy43pcQN/In9tDKV6ZzIA5kbC+lO3dzLHdzBjdzHXcxKc1keRw5b3q1cTzddzGY+u9k+7PoaP/XYhjpSO3/lKnroYRZzOZYHMJv5RAT70l7u4EY2s467uYX5aYmjRCdYPbahw2M1h7O6bNmb0zpu4AoADmFlTZ9bo6ce21Ap3svqVz22Ie9lk0t3TyfXbPoRnT1tzG5ezEmLH8+s5oV09bRzx44rWLPrGm7bfjmzm5ewcPrKqsrOsiUsYXbLUua0LOX6Tf9X0XY9qYeWxpkHMhrMal5ERNDWtZe7dl7J2t3Xs6n1Nm7fPpvVC8zYM9H27+/haedvYNuOHh5wYgtf+eRiTljdwu49Pbz3Y9v52Gd28tYPbOMBJ7Vw3rmVZxc4fnUz73/LAh529lSOP6aZObOzAJMtW7v50rd38/b/2Mblf27jX9+5hS98rP9I+K7OxNFHTuGl/zCbJz1mBsce3QzAnWs6ecv7t/G9n+zlE1/YxTFHNfPKC+aM3sFQ1erxPtZAA4exinksYg7zaWYqEUFP6mE7m7iNG9jHHm7kT5yTHseUaB7twzIpNAy/iiRJ42JB4fm9E1YLSaNlwYDXnteSxtw67srTIzZxCg9mZmQ/FjXFFI6JU1jEIQDcwU1VlbuF9Qc6VE7hHObGQgAao5GVsZrDWAXAndxMT+oZuqy0ni2sZxGHsIDaU0JqbNRjG9rKBrrz2cZO4RzmxAIispSx02MmJ3IW05gJwGbW1fKxNYrqsQ0NZwP3AFmWg976auJMhjbkvay+1WMb8l42udy75wbaunbTGFM4benTmNWcfddNDS0cu+BcFk/Pvuvbtv++qnJnNS/iUYf/E2cuezar5z+UpVVMh7Bi9ik8fMVLOHr+g5ndsvhA+5naNJPjFz6K5TNPAGDt7uvp7umsql4afZ/72m7uua+LmTOCH391GSeszjIuzZ7VwIffuZCnPm4GKcFb37+tqnKffN4M3vSaeZx9xrQDAQcAixY28sZXz+NNr84yJ3zrh3vp7Ez9tn3fmxfw18tW8G+vmncg4ADgqJVT+PZnl/DIh2Rp8T/66R01fWaNnnq8j02JZlbHqSyO5bTEtAPXoIZoYGEs41QeDGQZEbayocZPPvkZdCBJqhdTCs+7JqwWkkZL8ZwmpeR5LWnMbWQtAEs5jKkl5lI8nGMA2MNOWtOeqsudzxJmxdwS5WYjiDtoYzuby5bTlbq4hetopJHVnFrx/jV+6rENdZClNZ5CM1Nj8EiwhmhgJtkPcd2YVGii1WMbGkpHaj/ww+gyqp+fW6Ov3tuQ97L6V49tyHvZ5LJ+798AWDbzOKY2zRr0/hFzs9lRd3dsZm9H5ZlOIuJAR1215k5dRkOUT9y9fFYWdNCdutjbafaVifbNH2TXluc9fRbLlw3+3v7tVXMBuPbGdm69o2PU9nvGqVMBaGtLbN/Z/1pyzpnTaGws3f4iguc/O2vrd6/tYvsOr0MTqR7vY8OZHjNpyn8KbWf/MGvffxl0IEkiIpoj4ryI+EBE/Doi7omI1ojoiIhNEXFVRHw8Is4c5f1+OSJSRCTgnYW3Ht67vPgoU8YZEfGmiPhRRNwaEbsiojMitkfEzRHxpYh4ej6/fC11XBIR/xIRP42IuyJiT35ctkTEFRHxXxHxuIjSE8YVP2NEfHmYfU3I91CtiDi31PcSEcsj4m15PTdFxP6IuDsivhkRjx3hPpdGxFsi4s8RsTki2iLivoj4QUQ8vYbyGiPi7yLi6xFxW95u9ufH/BcR8dqIEn999i9jTaVtN39cMEx5j4+Iz0fE3yJiR+EzXhIR/x4Ry6r9nNXK2/sbI+K3+b7b8rr8LSIujIgnDrP9BYV28bsB75U6JueOsL4Tde16V/4Z7x7w1t1lPuclZcpZGhHn58f2z/l1pSMi9kbE2oj4WUS8ISIGZo2otr7zI+J1EfGHiFiff69rI+LH+XlQ268+jOy8j4hnFo5Rd0SsqGK/MyNid2H719X6GcqUf2REvDkiLi60q/aI2BgRl0XEf0TEQ4cpY3pEPDUiPpafx+vy49MWERvy7+ODEVE+33VWzsrCeVXME/rOIa43K0fhMEw6XamT3WSjUhZQOsXiHBYc+CGgmh8RdrAlL7f0aM6pMY0ZzM7XLV/uXfyVdvZzBMeV/MFdE6te29BUZgDZqJm2tG/Qtj2ph73sAmB2v5mNNN7qtQ0NZSNrSSSCYCkV34o1RiZDG/JeVt/qtQ15L5s8uno62N2+CaDs1AlzWw6hqSEbub59/9rxqtqQpjQWOiZTyZ8QNU727O3hmhvaAcpOnfCg06cyZ3b2U+1vLx+9Dtorrs7Kmj4tWLyw5E+1ZS2Y17d+tzEHE6Ze72PDaU276SLLsjItv+cdjMqHhkmSDgoR8STgq8C8Mqsszh9nAK+NiB8CL0op7RqnKpYUEYcBlwJHlFllXv44DrgA+GtEPDul9LcKy58CvBt4LVDqL+SF+eNBwD/ndTm38k8waH+T8nvoFRHPBL4I+V9mfVbmj+dFxA+AC1KqIgQ1K/vvgM8BA3O9LgeeDjw9Ii4Cnp1SGvZ/KnkH9BeBE0u8vSJ/PBZ4e0T8W0rpy9XUt1oRsQr4MuR5uPpbnj8eDrwtIt6bUvqPMarHv5EFUMwc8FYLMBc4FnhxRFxB9j3eNhb1qNT94Jz5InA+pYOApwAzgMOAx5N1Lr8hpfTpGvbzMODbwMCglcPyx1OAV0XE81JKVeV/G4Xz/sfAeuAQsuPwUuAdFe7+74HeITf7ga9UU/dyImIW8BHgxZT+v9KS/PFQ4I0R8ZWU0gUlynk58J+Uvn8ALM0f5wBviIgvAP+cUmof8Yc4iLXS18xmDGqWmYhgeprJbnbQyu6Kyu1IbXSSjb6ZWabc3vda2V223N1pB/dyBzOYxYp8ZIbqS722oUUso5mpdNDGX/gjx6a+ebD3p1Zu50b2s5cZzOYQVlZUJ42Nem1DQ+mdWmEhy2iOloq309io9zbkvaz+1Wsb8l42eezt6Et3P3NK6fj3iGDGlHnsat/I3s7q0uOPle37sxkdgwamN5f7mUDj4W+3dxyI+zhhdel57RsagtVHTeHP17Vz820jy3Swf38Pa9d18c0f7OEjn9oJwKteNKfqrBqXXZH9pLhkUSMLFzhee6LU632slJQSHbSxg63cmU/1MJXpLMynfzgYGXQgSVpJ/0673cAdwC6gkayjahXQ+5fa04EjI+LsSjp4h3Ej8Mv8+SrgqPz5DuDPw2w7h/4BB+15vbcDnWQBAccCvX/dngD8KSIemFK6daiCI2IOWWfYwwe8tQ24E9hLdsyOBXpDqecOU9/hrGTivocRyUczf5esbgm4GdhM1qF2XGHVZwCLIuKxldY5Ip4LfCt/2QX8lex7WEz2nfYejycCF5J1RA5V3mOAH0K/kNPWvM5tZMe4t3N2AfCliDgspfTeEsVdStb5WGnbHTQxZUScAvwaWFRY3AHcBOwhaxe9eW5nAB+MiKNTSi8t/ymrFxGfBV4+YPF9ZO19BlmAxtR8+dnAH/Lv8doB26yj75yeDxQzDPySwUaS83AlE3fO3EH2eaYBDyssvwxK5lC7ocSyk+kfcLCWrAN+L9kxP4asDZK//lREzE0pfaCKep4MfIe+7+52su9oHtl32hvG/3DgNxHx8JTSlkoKHo3zPqXUFRGfpy9byEsi4j0VTsXxj4Xn/5tSGvGkixFxKPAz4KQBb20A1pBdI3rvLb3Th8wtU9wx9A842EaWGWN3vu0K+s7tBrLzb0VEPCGlQcNy9tN3/jyQvnZ/J1lbLOWgzOXXnqftBWg50OwHa2EasKPf+pWXOzi1Y6/mfJ+lyk0pcQvXkkis5gE01JaASWOsXttQYzRxanowf+GP7GEnV/E7ggYaUtBNN01M4VCOYhUn0lA6+ZbGSb22oXL2pl3sYSfg1Ar1op7bkPeyyaFe25D3ssmjvbv1wPOpTQPHJPRpaczea+9qLbvOeOnq6eDundlPMUtmHM2UBoPoJtKGTX3/pT9kafkuyGVLmoD2futXo/nQOwZlJGhqglecP4f3/Xt1CSPXbejis1/Nxqic/5xZNU8DopGr1/tY0c3patazZtDymczlZM6i8SC+jxl0IEkCuI5slOhFKaVBHRgRsZRsNP8byO4dpwD/D/jXkew0pfRR4KP5Pt5FX8fTDSmlx1VQxHqyUbY/Aa4d2FEVEdPJOqE/QNZRNBv4JnB6uQLzNONfo3/AweXAW4HLU0o9hXWbyEapvoDBnVS1mJDvYRR8jazj8cfAa1NK9/S+ERFHAf9NNlobstHB7wf+pYJyFwJfArrJvsOPppR2FspeBXwdOCtf9LyI+J+U0h9KFRYRy8lGfPcGHLSRfa+fSSnL75h//48DPk1fh+B7IuK6lNJPi+WllM7Pt3kX1bddImIm8H36Ag56gP8APjTgc54NfJa+NvaSiLg+pfTJSvZTQT1eSf+Ag5uBV6aULiusMxt4PdnxaiT7br4XEacUR7CnlH5NFkRBZFMn/K7wXkXHpUoTde36OvD1PIV9cYqF81NKayospoOsPX4PuLhUBob8u/8gfYEN742IX5YI9ijnC2QBB5eTfac3FcpeStbeXpgvOo6snT2jwrJH67z/HFm7aiLLePAk4EdD7TgizgBOKyz6TIV1HqrMlny/xWv5/wHvSildV2LdR5Jl0Sn3P8lEFpj0TeDnKaV7S+zzSOBN9J1/jwNeA3yiX0EpbcrfI7KpOnrvT19PKb2rgo930Oih78+AhrJfDTTm73VT2Y9b3RWX21S23Hu5k93sYCkrmB+LK9qvxl89t6HZMY/T08O4kT+zhx0keg7MeN1DD1100kXngTSjmhj13IZK6f2xdArNLByUFEkToZ7bkPeyyaGe25D3ssmhO3UeeN4Q5buPGhuy97rSyEapj4a/br2Ytu69NEUzx8wfchY8jYPWfX1x9NOmlu+8nz4te29va0/ZdYaydHEjXV2we08P+9uyfb7i/Dm86dXzmDKl8qCBrq7EC/5pI3tbEyuWN/HvrzFTxkSq5/tYryam0EzLgXsXZAEHx3Iq02NW2e0OBgYdSJK+PFznZUppI/CWiLiBvlHnL89HpO4c6wqWcTuwMqXC/4YGyDuSvxARlwFXkQUdnBYRj8k7R0t5EfDkwusvAS9LKQ2azSsPcrgMuCzvQB6Jyfo9QNZp/h3geQNH6KaU7oyIJ5N15j0pX/zPEfH5lNLNw5TbGxzw3JTSdwa+mVK6IyIeD9xKX8f9i4GSQQfAh8lG30PWwf+MlNLPB5SZgJ/nc7VfQTa1AcBnI2LI9laDt9CXIQHgVSmlzw5cKaV0RV6f39PXIfqhiPh2SmnrSCoQEfPIjkuvm4GHDBw1nlLaTZbi/x6yjBKQZRp5B1mH/kSYzOcMwHkppb1DrZB/948iO3+eSNbB/XrgHyrcxyKydvOYgWn782NzfkS0Aq/MFz89z0hQKitFqbJHfN6nlNZHxI+BZ+aL/pFhgg7on+XghpTSnyqo73DeSv+AtPeklN5ZasX8WP6c7FpR7tr/7gq+37uAf4yIu8kCqwBenwdPjekMkhFxTbn3Hh3PGstdH5Ta037u5CaamMLRnDzR1dEkdV+6i1u5jmamciJnMY+FNNDIXnZxBzeykbXsYDNnpEcwLQ7eOURVuZQSG8li4paywlHrGpL3Mo0G72UaC3ftvJINe7OZVE9YdB7TpwycmVP3V2uvzRLgppS4d10X/33hLj7xhZ188wd7+N4XlvHwc8qPZi/657du4dIr2mhuhq9/aglzZh+8o9RVmWPiFI7hFAC6Uidb2cgd3MjVXMKKdDTHxCkTXMOJ4/8oJOkgN1ynyIB1vw38MX85g2ze+wmRUmqvtAM4n3u+2DlZciRvRDQA/15Y9BfgHyvp/KnmOI50+3r6HnI7yDrMB6YEByA/fi8jSxsP2d8fr6iw7G+WCjgolL2DbDR3r5Ih7RGxDCj2pH12YMDBgHLvJRsh3+sQ4O8qqnEFImIq/bML/LxUwEGhPrvIAmJ6j/E0+ne81uql9J9q4sVDpalPKfVmFjmwfcTE/Bo0yc+ZiuufBzcVMwQ8JaLiPG2dwEsHBhwM8HqgOAr/nyosezTP+08Vnp+XZ5AoKSJmAc8tLBqNLAezyDIM9PpZuYCDgcp9j1XeEz5ENp0JZNMunFHFtipoKMTU942bG6w7f6+xwhj8xorL7SpZ7i1cTzddHMkJtET59JCaePXahnamrdzCtQQNnM7DWBqH0RLTmBLNzItFnM7DmcEs2mnjDm6sqE4aG/XahkrZxkY68rSxTq1QP+q1DXkvmzzqtQ15L5s8GqMv00TPEDPfdfdk7zVFc9l1xtra3X/htu2XA3Ds/HNZNnP1hNVFfWZM78sy0JuBoJR9+7P3Zs4YWTdlRLDi0Cl8+J0L+ei7FrJ9Rw/P/6eN7Ns3fAaFt75/G5/96m4aG+Fr/7OUBz+wskAFjZ16vY+V0xRTWBqHcQaPoJEm1nI7m9OgGXYPGgYdSJKqdUXh+QMnrBbVq6TeDwSOLrx+3yiPbB9N9fQ9fCOltH2oFfJR1d8tLHpmuXUH+J8K1rm08HxVRMn/8T4Z+uVo/GgF5f4QuKvw+ukVbFOphwHFCeaGrU9K6RrgklGuT7GMy1NKV1awzUcKz+cCjxiFeoyHejpnqpJSuh3Ylr+cCRxf4aa/zIOuhip7P/0Ddx6fT00znFE771NKvwVuyV82kAUrlPMPZMcAsoCGr1dQ1+E8kawt96oo4GC05NP2FM+9MW+fKaXTyz3Get9jqTjf41DzL7azf9D6Q5fb98NT77al9HbeFcvdnjazhXXMYDaHcDhdqavfI9H3Q1jvsp5UW3pRjVw9tiGAtWQzCC1kacl0nQ3RyKF58qQtbKBMPJjGQb22oVI2kM2MNIPZzA7TCNeLemxD3ssml3psQ+C9bDKZ2tiXzK2tq3wsdXt39l5L08RkpVi352Zu3vobAFbNO5uVcyf1f2XuVw5Z2tdZu35j+cCVDZuy95YtGb2E7C97/hxaWoL1G7v5+W/3Dbnu+z++nQ/+9w4i4LMfWcyznjTSJLYaDfV6HxvO1JjG4jxhbu8UZgcjp1eQJB0QEYuAx5DNe34I2XQELQNWW1V4fug4VW1I+ajxRwMPIEtXP5tsJHhxAq/5hefl6v3wwvN2svm8x90k/B7KZgwY4CKy0foAh0TEoSml+4ZYv5NsWozhFMsIYA6wZcA6Zxee/y2ldOdwhaaUUkT8H/C6EmWMVLGsvcDvKtzux/R18p8SEdPzaUSqlgdnnFZY9JNy6w7we7JR7r2/Tp8N/LSWOoyWSXjO9BMRDwAeTBZMMB+YBYMmmCv+knMoVDT8p5pz89358yaydnH5KJZdyXn/aeC/8ucvjoh35lkeBipm+PhWSmlPhfUYSvHaf3dK6epRKPOAiFgBPBI4GVhC9v0ODI46qfC8rtrnZDKDvh+wW9nd73WvlBL78gQcM5hdUbnN0cKU1EwnHexlNwtYWnK9veweVG4b+w7U5xJ+XHYfbezjknxmkeM5g0NYWVHdNLrqsQ311gVgGuV/1O99r4duOmiv+kcyjY56bUMDdaYOtrAegEPMclBX6rENeS+bXOqxDfXWBbyXTQYzmvt+PtvbuY2ZzfMHrZNSorUzS5I4c8qCQe+PtY17b+WmLb8AEivnnM6qeeeMex1U3rGrmomAlOCvt3awetXgsUE9PYlb78zGeR1/zOhly2hpCRbMa2D9xm7uuqf8OLKPf3Ynb/+PbBzFx9+7kBc9t7JrocZevd7HKtEb2LCf8gFb93cGHUiSiIjDyeZ1fzrV3RvmjkmFKpSndX872Xzk1fwlMLfM8uMKz29IKXXUWLWaTNbvgco6P0utdwz9AwYG2lZhponWAa9LjdIudjj/pYIye91QeH5IREzLR4aPVLE+N+UjnautTxNwOPC3GutwGP075is6Lnkwxo1k2Rqg/2cZV5P4nAEgIp4CfJD+155KzK1wvUrPzb+STd3RG6h1DMMHHYz2ef8V4P1kwRVLgacC3y+uEBEPBE4tLBrx1Aq54vEftYCDiDgR+BhZUFwMs3rR3NGqw8GmKaYwO81jNzvYxqYDowyKdrGdLrJby3wWV1z2PBazmfvYziYO55hB77el/Qd+TK+mXNWXem1DkV9Cejv+Sim+1+RPPROmXtvQQJu4lx56CIKlBh3UlcnShlS/6rUNeS+bPJoampnTspRd7RvZtu8els44etA6O9s30NWTzeI3f9qKca3f5tY7+cvmn5FIHDbrFI5dcO647l/DmzWzgTNOaeGq69u5+LJ9POOJgzMIXHltG7t2Zz+FPfIhozelwd7WHrZsy1Lnl5u24dNf2cXr37UVgA+8dQGvfsncUdu/Rq5e72OV2J//RF3p1Az3RwfvJ5ckARARZwK/orZOjoEjicdNRCwELiYb2VytciG0xfDtzTWUW7PJ+j3ktg2/Ssn1hsvjOtQ89EMp1blX3NfALAhDGbjuPBgiB1flRrM+o1GHkdRjQvLxTvJzhoh4H/DWGjevtP4VnZsppbaIaKVv2oJKvtNRPe9TSrsi4pv0Ta3wjwwIOgBeXnh+VUrp2grrMJxRv/ZHxBPJ6l9LW5vw9jmZLWUFu9nBRtZyZDqOluj/A9Y9ZDOOzGIeM0qk9i1f7mFs5j62sYk9aSezYm6/99fm5TYzlXmFHycOiZVDjvS8M/2Vu/kbU5nOQ+IJFddHY6fe2hDATOawl11sZSNtaT9TB9QppXQghecMZtMY/tQzkeqxDQ3UO7XCfJbQEo4krjf11oa8l00+9daGwHvZZLNs5rHsat/I+r1/46h5D2JqU/9O4zU7s1jt2c1LSmZCGCtb963h+s0/IdHDITNP4PiFjxq3fas6z3v6LK66vp1v/mAPb//X+YOmUPjop3cCcPrJLSUzIZTT1ZVoaiof0/+Jz++kMx++9JCzBv+N85X/3c1r3pz9pPX2f53HG1/tFFP1qB7vYz2ph4YoHcgCsC/tOZBJbC4LK67T/U35IyRJut/LMwX8gL5Ou06y+bGfS5bqeT4wNaUUvQ/6UnBPtM/TP+DgErJOqjPI0ldPBxoK9a5k3vniX6PlJ40aZZP8ewCoNCPEwCCC8exYK+6rmgwWA+s8Wr/K1kN9Bh7/Wusx7r9UT/ZzJiKeSv+Ag3XAe8hGxB9Jln6/aUD976lhV7V+p5Wcm2Nx3n+q8PzREXFU74uImE32/fb6bIX7r8SoXvsjYjnwHfo+6z6yrAxPJ8uqMBdoGfD9fmWk+1VmOUcylel008X1/IG9KRul0JU6uT3dwBbWAbCKEwZte3H6Hhen73Fn+uug9xZxCLPz+JQbuIJdKYun6Und3JNuYy23A3AUxw/5Q4TqXz22oUM5EoBuuriO37M9baYn9WSpjdMe/sIf2U2W4viwiUtApFw9tqGi1rSHXWTphJ1aoT7VextS/avHNuS9bHI5bNbJTG2aTXfq4NqNP2RvR/Zdd/V0cOu2S9m0L/uuj5n/kEHb/uKuj/KLuz7K7dv/WLLszu42Orr3HXj06urp6Le8J3X3225H2zqu2/RjelI3S2es5qRFjyWimoRyGk8vf8FsDj+0iT17E095wQZuvjX7CWHP3h7e9N6t/PBn2Yjw97158PQcjcvuoHHZHbz7I4PHOpz48LV88sKd3Lmmk5TSgeW33tHB6962hXd8KPsb52mPn8FJx/X/+eH7P93Ly/51MynBv71qLu96w/hPDaLK1ON97Dau59Z0PTvTVroL16fO1MH6tIaruZQeummkiRUMzhBzsDBkUJIObi+ib+7oTuAxKaVLh9mm8vDBMZKnrH5aYdFbUkofGGazSuq9o/B8TrX1GoFJ+T0UzKL/sStn4BQYu8agLuXsLDyv5tgNrPPOUivVoFjORNVn4La11mMkdajVZD9n3l54fhVZ/Yc7H2qpf63faSXn5qif9yml6yPiCuBssowlLwP+PX/7+XBgAthdwLcq2HelRvva/y/0r+s5KaWbh9mmntrnpNYYjZySzuFaLmMPO/kTv6IxNdFN14F1VnEiC6L0/I3lRAQnpwdxDZeyn1au4nc0piZ66CaR/di1nCNZHkeO6ufR+KvHNjQ3FnJ0OpnbuYFWdnMtlxEEQQM99P3gtZwjONQ2OOHqsQ0V9WY5aGIKizikyk+n8VDvbUj1rx7bkPeyyaWxYQqnLXkqV234Lrs7NnP5fV+mKZrpSp2Qf9fHzH8IC6evrLrsP6z7Gm1duwct/8vmn/Z7feayv2PBtMMOvL59+x/oTlkb3rZ/Lb9bW362veMWPIJlM4+tum4aPdOmNfDDLy/jMc9ex7U3tnPSuWuZPauBva099PRABPy/Ny/gvHNLzZBa3u13dfLat23ltW/bSktLMGtG0Lovsb+tLwDhcY+czlf+e8mgbd/03q1055ebr313D1/77p6y+/nehUs558zRm/ZB1anH+1g33WzgLu7lDgCa0hSAA9M8QJYh4WQexNSorl3fnxh0IEkHt8cVnn+rgk47yOaBn2jFeq8hmxN9OJXUe0Ph+epqKjRCk/V76HUElXU+DvyLbdMY1KWcYsr0o8quNVhx3Q5Gr4N9NOozsJyR1KG37CtqqMe4TkWSm7TnTEQsAk4vLHrTcAEHETGT2qaROAK4roI6HQpMKSyq5Nwcq/P+U2RBBwAvioi3p5Q66T+1wtdSSuUng63eaF/7i+3zvyoIOIA6aZ/3F7NiLg9K57GGW9jKBtrZzxRamMM8VnA082PwD1CVmBrTOSs9mjXcymbW0UYrjTQxi7kcylEsiUOHL0STQj22ocPjGOalhdzLXexkK+3sI5FoYRqzmc9yjmBhlT+6aezUYxuCLH15b9DBEg6jIRprqofGXr22IU0e9diGvJdNLrNbFvOQQy/grp1XsnnfXbR376W5YSpzpi5j5ZzTWDBtfLPl9HYIAnT2DD3rZW9wgibWKSe0cMMlK/jgJ3Zw0cWtrNvYzYJ5jZz5gBZe9/K5POqh1XfM/ugry/jt7/fxx6vaWL+piy3bupnSFKw6YgpnntrC3z9zFk941IyS2/b09D3ftKW75Dq9OjrTkO9r7NXbfWwlq5nBLLazhf3spYM2ekg008JM5rCApSznCJpiSsntDxYGHUjSwa34P4Q/D7dyZHnLzhm76lSsWO+rUzGfVnmDc74NdgXw2vz5YRFxZErprqprV73J+j30OguoZG71swrPO4Abx6Y6JV0D9E4wekZETMk7ModTPM7XpZR6SqxTXFZpbr9rCs+PjIglKaVKOnqL9dmUUrqvwv0NklLaEhH30tfZeQ7ZFAVDiogF9O+YvbrWOoxAvZwzA9tDJd//igGvh60/Wd1ryXF7Ftk0FJWsV3RNybUGbzMW5/13gY8Bi4DFwNMj4h76T6czmlMrQHbtf2r+/IyImJlS2juC8qptnzPp//mGUsv15qDUElNZzams5tSKt3l0PGvYdZpiCqs4kVWcOILa9TkqTuCoEikhNfHqsQ3NjvmcwPjNm6yRqcc2FBE8lCdWvZ0mRj22oVK8l9WvemxD3ssml5amGRy38JEcxyMr3uZxR75+yPfPXfGymupy1iHPqWk7Tayli5v4+PsW8fH3Lap4m+4N5adYefJ5M3jyeaWDCoZz11Ura9pOE6ee7mMzYjYzmM1KzKIyFCfokqSDW7Whd48Dlo9FRapUVb3zTtKnVbDqb4FiR/Qrq9nPCEzW76HX8ypc7+8Lz69MKQ2c630sFUfCzwGeMtwG+Yj0x5cpo6i18LzS3GvFsoIsdfxw9Wmh/5z2lYzur6Yez4qIqWXX7PN8+v8Nedko1KNa9XLOtA54Xcn3X0vI80tq2Abg7yIqGsL4D4Xn96aU7q5gmzE57/P3v1hY9I/5o9cfUko3VbjvSv2i8HwacP4Iy6v2O34B0FzhurVcbyRJkiRJkqQxZdCBJB3c1heeP2yoFSNiOvCfY1udihXrfXZEDJe55z+poHMmpbQF+EZh0Wsj4vRy64+iyfo99HpoRAw5ZCoingOcVlh04dhWaZDfAncWXv+/CjrYP0hfR2ACPl9mvWJq9qPyUfVDSindCfyusOjNEbFwmM3eCBRzh31uuP1UoPiZFgFvHmrlPIDnrYVFl1eYOn601cs5sxNoK7w+uoJt1g94PVz9HwU8u7pqHbASeMUw5Z9N/6CsSs/NsTzvP0PfiP5H0D9wofzEnTVKKf0FuKSw6L0RMTAjRTWqaZ9LgPdUUXbxelNJe5MkSZIkSZLGnEEHknRw+23h+bMi4kmlVso7Gn/K6Mx1PRqK9V4OvK/UShHRFBEfIRtFWqn3AL3zq08BfhURjx5qg4g4LCJeXcU+Bpqs30PR1yLizFJvRMRDgS8UFt0BfGdcapXLp+AoduytBr6bpzXvJzJvAV5cWPyNlNIdZYovpqKfD7yowmq9Bw5MSrgAuCgiFpdaMSJeCLyrsOj3KaXfVLifslJKl9E/+OFtEVGy/nlQxE/JghMgq/u7R1qHGtXFOZNS6gauLyx61XDBLCmltfQPgPlIXs9BIuJc4PuMLI3+R8sFB0TEcQPK3w58uoqyx+S8TymtAX7eWxTQkj/fBnyvivpV49/py3QzD7g0Ih4w1AYRcVyZ86XYPv8pIs4os/0K4NfAcAFHRcXrzXkRcVIV20qSJEmSJEljYriRoZKk+7fPAW8CZpIFov04Ir4G/ATYRNbx8lCyztcFwG7gIipPqz0mUkqXR8SfgQfmi94UEWcBXwHuIstqcApZ52/vREufYZgRv3nZd+edSN8FGsk6kX8dEb8BfgzcBuwjOzYnAo8CziWbp/yTNX6kSfk9FHyLrC5/zOt9EbCFbFT+k8hSt/emeO8CXpJSaitV0FhKKX01Ip4M9E7u9STgrxHxeeBqoB04hiy1+tmFTdcArxmi3Fsj4mqgt2Pxwoh4M3A72Rz2vT6RUvptYbtLIuJjQO+Ehw8Ebs7r8wdgD9lI9eeSTQ/Qayfwwso+dUUuAK4ja+sNwBcj4nlk3+vdwHTgHODl9AUcAPxXSuniUaxHNerpnPk68KD8+XnAhoi4Pt9nb1DJTSmltxW2+Sjwqfz58cCNEfEp4EqyNnM48FTg6WSd7j8ju95UO/q+99z8aUR8D/ghcB/Zd/0YsuNTDJJ4TUppc5Vlj9V5/ykYNOn0V8bq2pFSujIi/g34r3zRSuDqiPg/suO/huwasZDs/vJY4Cyy+8KXBhT3cbLzqhGYAfw+Ir5AFmCwHVhMdu+4gOz8upfsHvKECqr6/bz8qfm21+ftbT3QXVjv5VV8l5IkSZIkSdKIGHQgSQexlNLmiDifbPRpE1nn3fmUns+6lazz86zxq+GQ/gH4I32doOfmj4F6R2NfSgVBBwAppR9GxFOAbwOz8sWPyh+jbpJ/D5DNt34McDpZoEe5kf7dwPPz0fUT5flkbaI3Xf0K4L1DrH8L8NiU0s5hyn0ZcDFZBzfAqvxR9KMS272BrGPydfnrBWQjrsvZADwuHwk+KlJKayPi4WTz2i/PFz8mf5TzSfqCJcZdnZ0znwWeTNYJDTCXwdeiuQNef4bsevLM/PUyyrfDa8mud9fXULe3A3PIOrOfRV/ATSlvSCl9s4qyx/q8/wVZENmRhWWfrbKMqqSUPhERrWTZHqaQtaun0X/6iUrKuSki/pW+AIapwKvzx0BbyIJLygY2DSh7a0S8kmxqlN62fxr9p7GAvmuKJEmSJEmSNOacXkGSDnIppR8AjwZuKrNKN/Ar4LSU0s/LrDPu8lT3Z5CNQC3nRuCJKaWqU8CnlH5GlpL9f8hGnJfTDfwe+H/V7mPA/ibl9wCQUtoDPJisg621zGpXAmellMZ1WoWBUkrtwHPIOqH/NsSq24B3Aqfn6fCHK/d64IR8m9+TdSR2DLVNvl1KKf0L2Qj5q4ZYdS/Z6OYTU0o3DFdutVJKNwEnkY3A3z3EqtcAT0gpvSal1DPa9ahGvZwzKaUusk79vyfLJHA32XmQhtgmkbXDd1D+eO8APgicXUHQSzndZAERbycbYV/KLcB5KaWPVFPwWJ/3efsqtvXfpZRuq7acGvZ7IVn2iW8AQ2VV6AB+SZkMNymlT5AFeawZYvvvAienlK4ps065On6ZLNjjf8iylOwkyyYhSZIkSZIkTYjIfvOUJB3sIiLIRkqeQTbaeg/ZqOrLU0obJ7Juw4mIlcDDyEYLd5HV+/qU0s2jVP4UsvTpR5NlVmgg6+S5A7g6pbRjNPaT76vuv4d8nvnf9b5OKUXhvRnAI8kyCMwiS3X/x5TSreNby8pExDFk0xosBprJggVuBq6ciE71iDiMrCN3Kdk0IdvIpmn4Q0pp2CCGUarDlLwOR5Olkt9P9j3+oZIAjPE2Gc6ZoUTETLLr1zFk3/kWso7qS1NKnaO4n2ayDAxHkE2vsAW4NqV07SiUPernfUQsBNaRnZcAz0kp/e9I61plHaYCDyE7ZguBHrLgjdvIrv3lgi2KZTSS3T9OJct4sYPsc106gmCSMfWYhmf7H0RJkiRpBJqOOHyiq6BJ7qI//Hiiq6BJ7rGHnDrRVdAk9+ue78bwa/Vn0IEkSarKUEEHkjQaIuJNZJkeADYCK0YzCEPlGXQgSZIkjYxBBxopgw40UgYdaKRqCTpwegVJkiRJdSPPnPDawqLPGnAgSZIkSZIk1S+DDiRJkiTVhYiYBnyabLocyKbL+O+Jq5EkSZIkSZKk4TRNdAUkSZIkHbwi4n3AicAM4BRgUeHt96aUtk1IxSRJkiRJkiRVxKADSZIkSRPpIcDDSyy/CPjYONdFkiRJkiRJUpWcXkGSJElSvWgFrgb+GXhqSql7gusjSZIkSZIkaRhmOpAkSVVJKV0CxETXQ9L9Q0rp3ImugyRJkiRJkqTamelAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNXEoANJkiRJkiRJkiRJklQTgw4kSZIkSZIkSZIkSVJNDDqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNXEoANJkiRJkiRJkiRJklQTgw4kSZIkSZIkSZIkSVJNDDqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNXEoANJkiRJkiRJkiRJklQTgw4kSZIkSZIkSZIkSVJNDDqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNXEoANJkiRJkiRJkiRJklQTgw4kSZIkSZIkSZIkSVJNDDqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUk6aJroAkSZIkSbp/aHvyAye6Cprkpv3qLxNdBU1yqb19oqugSa5h+vSJroImuZ45Mya6CprkHnvIqRNdBU1yH17zp4mugg5CZjqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNXEoANJkiRJkiRJkiRJklQTgw4kSZIkSZIkSZIkSVJNDDqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNXEoANJkiRJkiRJkiRJklQTgw4kSZIkSZIkSZIkSVJNDDqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNXEoANJkiRJkiRJkiRJklQTgw4kSZIkSZIkSZIkSVJNDDqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJEmSJEmSJNXEoANJkiRJkiRJkiRJklQTgw4kSZIkSZIkSZIkSVJNDDqQJEmSJEmSJEmSJEk1MehAkiRJkiRJkiRJkiTVxKADSZIkSZIkSZIkSZJUE4MOJEmSJEmSJEmSJElSTQw6kCRJkiRJkiRJkiRJNTHoQJIkSZIkSZIkSZIk1cSgA0mSJEmSJEmSJEmSVBODDiRJkiRJkiRJkiRJUk0MOpAkSZIkSZIkSZIkSTUx6ECSJEmSJP3/9u47TrKsrhv/53SYnMOG2ZzYvOwuOYogCAYwgCigoGBOjzw/gj4qmADBAKKoIIgIpkcRE/CQJLOSdmF3YXOOk3dyx/P741ZPV/d0rOme6WHe79erXl1177mnTlWduvd2ne/9HgAAAICOCDoAAAAAAAAAADoi6ADgOFFKeUkppbZudxzt9nB8KKU8pa3f1SnK6Z8cUaWU17b1uU8e7fYcj3zvD18p5d1t7+G7j3Z7AAAAADg+CToAAAAAAAAAADrSc7QbAADtSimvTfKa1sNP1VqfcvRaM71SyplJbm9bdFat9Y6j0xoAIEn66oHckRuyNfenL/vTk96syrqcnnOzrpzYcb2DdSB35MZszr05kH3pTndWZHVOzTk5sZw6q7ruqjfnpnwtSbIky/LE8l0dt4u5139gd+6++RPZ8eA303dgV3p6lmTF2tNyytlPzJqN5826vuGhwTy07dbs3nlP9uy8O3t23J3+vt1Jkosf+9KsPeH8Kbf/0kdfn779O6Ysc+ZF351Tz/22WbeN+dFX9+f2weuzdfje9NV96cmirOpan9O7L8j67pNmXd9wHcr24Qezq27LruHteWh4W/qzP0lyRe+3Z0P3phnV8+DQnbl76ObsGd6RoQxlaVmeE7pOy5k9F6en9M66XcyfhXYs+2z9YA5k35R1n5dLc0aZen/GkdM3vD+3D1ybLUP3jO6HujfkjN4Ls7775FnXN1yHsn3ogewa3paHhrdm1/C29NVmP3Tl4qdlQ88pU26/e3hHdg5tbrYf2pq99aHU1JzUfWYuW/Lkjl4j86tvYE9uf/Cz2fLQzekb2JWe7iVZtWxTzjjhMVm/8uxZ1zc8PJjte+7Irn335aF992XX3vvSN7gnSXLlOS/IhlXnTrHtUO7dfnUe2ntfdu9/IP0De9I/tC9dpSfLFq/LhlXn5PSNj87i3pUdv17m1kI7ju2uO/NQtmVXdmRXdmRvdqWm5sScmkvLYztuD/Nn6+ahvOttu/OZj+/P5geHsmJlVy55+KK84CdW5DFPXDLr+rZvG8onPrw///PZvtxwXX82PziUrq6Skzd159FPWJwXvHRlTj9z6mHzW28ayN++fXe++IW+bN08lOXLu/Kwi3rzgy9Ynmd8z7JOX+q3DEEHAAAAfMvYXXfmq/l0BtKfJOlOT/rTl625P1tzf86tl+TMcsGs6z1Q9+Ur+VT2Z+/BegczkB3Z0tzq2bmgXDnjum7N9bNuA0fG3ofuz7Vf+MsM9jeDa909SzLQvzc7Hvxmdjx4Q8648Jk57bxvn1Wd+/ZszvVXvfOw29bTuzSlq3vCdd3diw67fubG7uEd+Ur/xzOQviRJT3qb/dDwvdk6fG/OrZfnrJ6LZ1Xn3vpQrh7478Nq1zcG/if3Dt2SJCkp6Up39tZduX3o+jwwfGceuejpWVL8WLoQLORjWU960zVJ8txuPzUvGLuHd+TL+z9y6H5o6J5sHbon5/VekbMWXTqrOvcMP5Sv9n284zZd1/fZ7B6eOoCOhWP3/gfz5Zvfk4GhJrCkp2tx+gf3Zeuum7N118057+Sn5qyTnjirOvcc2Jqv3vp3HbVnYGh/vnn3Bw8+Linp7l6cwaED2b3/geze/0Du3vrlXH7WD2XdyrM6eg7mzkI8jl2fL2VPHur8RXFE3fTN/vz0j2zNzh3DSZIVK0t2bh/Opz9+IJ/5xIH8witX5Sd+btWs6vzOR9+fwcHRx8uWlwwM1Nx+62Buv3UwH/jHvXnNm9blWc+Z+Hz4g/+6L6995fYMNN06K1eV7NkznC9+ri9f/FxfPv3xA/mdP1qbUkpHr/lbgTNBAOCoq7W+O8m7j3IzADjGDdWhfC2fz0D6szJrcnEelRVldQbrQG7LN3JXbs4tuS4r65qsLzO/0rjWmq/nquzP3izJslySR2dN2ZChOpS7c0tuybW5J7dlZV2TU8r0V33dmGsylMGsyrrsyvbDecnMsaGhgXzji+/OYP++LF+9KQ+74oezfNVJGRw4kLtv+ljuvfXTufObH86K1adk7QkPm1Xd3b1Ls2L1KVm55rSsWHNqbvjy3866fRc86seyZsM5s96OI2eoDuaa/k9lIH1ZWdbmkt7HZ0XXmmY/NHht7hz6Zm4ZvCaryrpZX2ncZEtYl1VlXVZ1rc/XBz4z423vHrypFXBQcl7P5Tm9+/x0le7sHN6Sa/s/l/11T67t/2wetfgZs3zFzLWFfiy7LI/LunLCXLxU5slQHczVBz7R7Ie61uXSxU9s7Yf6c2v/13Pn4Ddy88DVWdm1Pht6ZpYlZcTIfmh194as6lqfr/V9asbblnRlZdfarOpan9VdG/Lg0F3ZNnTfbF8eR8DQ8ECuvu0fMjC0PyuXnpRLz/i+rFh6QgaH+nLrA5/KnZuvys33fyIrl52cDatmd17S070kq5aenNXLN2XVsk352u3/d0bbdZWenL7xMVm74oysXn5KFvesSCklw8ND2bb7ttx070ezt29rvnb7P+eJF/1ientmfxU0c2OhHse60pUVWZNVWZtVWZstuTfb8uBcvnTmyIEDNf/rZduyc8dwLri4N7/75nU552G92bN7OG9/y6787Tv25E/fuCsXXrIoj3vyzL/rg4PJlY9ZlO97/vI87klLsuGE7gwN1Vz71f684Td35sZvDOQ3X7495zysJw+7cGxA9zeu7c9rXrE9gwPJk79jSV712jXZdFpP+vtq/v2f9+aNr92Z/3r/vpx1bk9e+vOzC4b4ViLoAAAAgG8J9+a2VorNnjw8T8iSsjRJ0lN687A8PPvr3mzJfbkl12V9Zv4D15bcdzA44OF5fFaWNUmS7tKdM3N++ur+3J1bcmu+kZPrmekqE18BmiRb6n3ZkvuyMZuyIqsFHSwwD9xxVfr270h396Jc9Ogfz+Klq5MkPb1LctbF35P9e7dl+wPX545vfmhWQQfLV52Uxz7ztcf1VS/Hi3uGbsmB7E13enL5oqcczBzQU3rzsN4rs6/uzpbhe3Lz4DWzCjpYUdbmKYufO7YPDcxs2+E6lNsGr02SnN59fs7suejgujVdG/PwRU/O//R/KDvrlmwZuicbu2c3XQxz61g4lrGw3TN4Uw7UZj90xeKnZknXyH5oUc5f/Mjsr7uzeeju3Dzw1VkFHazsWptvX/b8sfuhvpm36zFLnpXS1q92Dm+Z+cYcUfds/UoO9D+U7q5FueLsH86SRc0AWk/34px/yjOyv29HNj90Y26+7xOzCjpYufTEfPulr+jofKi3Z0kuOPU7D1ne1dWdjavPy/Il6/PZb/xpBob2Z8uum7Jp3WWzfg7mxkI9jj0qTx3T9x6q2w7zlTJf/uV9e3L/PUNZtrzkLe/akBNOajK9rVjZlZf/+prcc9dg/vv/Hchbf/+hWQUd/NU/bcwjHrN4zLLu7pLLH7U4b3vvhjzvGQ9m+9bhvO+de/Jbf7Bu7LZv3ZXBgWTTqd1509vWZ9Hipi8tWlzy3BeuyLatw/mLP9qVd/3Z7jz3hSuyes3xeR51fL5qAAAAvuU8kLuSJCfltIM/brU7I80g8e7szN66e9b1rsuJB3/cGltvM391fw5kezZPWs9gHcwNuTrd6c75uXzGz8+Rs+Xeq5MkG0+94mDAQbtTz/22JMneh+7Nvj2Tf9bjldIl4OA48cDQ7UmSk7rPnHCqgpEB/911e/YO75pxvaWUjvvQtuEH0p8DSZIzei48ZP2qrnVZ19X86H//0B0dPQdzZ6Efy1j47h9s9kMn95x1MOCg3Zm9zfQuu4e3Z+/wzFONH85+qNneUMSx4v4d1yVJTl57ycGAg3ZnnvD4JMnu/fdn74GtM673cPvQVJYtXpee7mbwsW9g5vtG5t5CPY45Fz92fPADzTR3z3rOsoMBB+1+7KdWJkm+ed1A7rh1hlG4ySEBB+3Wre/OE7+92Yd889qxdQ4N1Xzh002U3fN+dMXBgIN2L3rpipSS7Ntb84kP759xm77VONIDC0YpZVEp5RmllNeXUj5aSrmzlLK3lNJfSnmwlPKlUsqbSymPmqfnf0oppY7c2pZvKqW8spTyuVLKPaWUgVaZp0xSz9mllNe0yt9XSukrpWwtpVxbSvnTUsqTO2xfdynlB0op7yilXFdK2dJqy65SyjdKKf9QSvmpUsr6zt6BQ57v/FLK7W3vyf2llCumKH9F67P7Ytvr3lZK+Xop5S3TfW6llDta7/tr2hZ/W/tnMu72kjl4jUtKKT9aSvnnUsotpZTdpZShVr+7q5TymVLKW0spzy1l7C+GpZTXttp7+7hqb5+kvZ+cpA0nlVJeXEp5Z+u929Lq83tabfhgKeUVM/1cSylnjnveM1vLl5ZSfryU8rFSyt2tz2dzKeWTpZRfLqXMKu9cKaW3lPKy1nf1vlLKgVZ7P95aPqvJYEspL2lr8x1TlHvtRO9p63v3ulLKNaWU7aWU/a0+9b5SyuwmXc7B/v8Hre/aQ62+cUMp5d3t3+HW45H2vHu2zzPDtjyhlPInre/S1tZnd38p5QullN8qpZw7w3omfI9bffDXWv1vc+uzvKeU8v5SyvfPx2tqe9456/vzqZTSVUp5Umn27f9VSrm11ScGWu3+WinlL0opT5tFnXeUWe7PZtrfJqu7lPKsUso/lmZ/t7+UsqOUcnUp5Q2lzCKn4Wh983Jcmos+Odl7VUp5cmvdDa3PcGfr8/u9UsoZs2jjvJ6zlGZf/rpW23ZOtg9irME6kF1p5gme7IqZ1VmfnvQmyawGVHZkS6veEydcv6QszfKsapWdvN7bcn36sj9n5ULzpi9Ag4MHsmfnvUmSNRsnzmKwcu3p6W6l631oyy1HrG0cGwbrQHbV5gq89V0TZzFYXTaM7oeGHzgi7dox3KQOXlHWTLrvWd+16Yi2iYkdC8cyFrbBOpBdw83Vu+u7J85isLpr48E+tG3o/iPWNo4Ng0N92bWvmfZi/SRZDFYvPzU93c3g3bbd438WOzr2HtiawaEmwG7pojVHtzHHMccxDtfePcMHB/0ny2Jw2ZWLsmJVM/D/xc/NIuXONEayEwwN1THLd24fzoH9zbIzzp54AoHlK7qy8cQmQOKqzx6YszYda0yvACwIpZTvSfKeJGsnKXJC6/bIJL9cSvnXJD9ea515SHZn7XpBkj9PMu1EPKWUniSvS/LLSRaNW72+dbskyc+XUj6Y5CdqrTOaOKqU8qwkb04y0a+fK5Nc2Lo9P8mflFIeXmu9cSZ1T/J8j0vyH602J8nNSb6z1nrIfxKllBOS/FmS505Q1brW7dIkv1RK+fskP1lr3dtp2+ZK6zW+L8lZE6xe1rqdluSJSX4hyf9N8kNz3IZ3JXlxJg4C7E2yvNWGZyV5TSnlFbXWP+/geS5L8g9p+ki7jUm+rXX7hVLKd9Zab5tBfRcl+cc0/bndaa3bU5P871LK82bb1k6UUn4xyZuSjA9XPaN1e0Ep5R1JfrbWOjSD+v5Pkt/Mod/j81u3F5dS/irJLx5u26dpxwlJ/irJ906w+qTW7bFJfrWU8tYkr6q1Ds7yOX4oyduTjL+U85Qk35/k+0sp/5XkebXWOQvTPVJ9fy6UUh6ZZn842aD8htbtsiQ/XUr5TJIfrrUumMlJW4P+f51D+9KSJJe3bj9fSnl+rfWDM6xzXo5L89UnSxNY9dYkL5tg9WWt2y+XUl5ea337NHXN6zlLKeVnkvxhmuNQuyO6DzoW7c3oVTLLJzl1K6VkWV2RXdmRvZnZFcb99UAG0p8kWTHFKeGKrMre7Jq03l11R+7OLVmelTl9wq8OR9v+3ZuTND8mLVs18Y+ZpXRl6YqN2bPz7uzbfeR/zLz9uv9I34GHMjRwID2LlmbF6lOy8dQrs/GUh7uCdAHY27arXzHBFXhJaz9UVmVX3Tam/Hza03qe5eXQ7B0jVpRm/zaQvvTXA1k0u5hk5shCP5YlyU35evrqvgxmIL1ZlJVZm5Nzek7Maa4iXQDaMxes6FozYZlSSpZ1rc6u4a2zynTA8aE9c8GKJRsnLFNKybLF67Nr332zynQw12qt6R/ckx177srN930iSbKkd3U2rnaufbQcC8cxFrbbbxlMbY35n/OwiYewu7pKzjy7J9ddM5Dbbp55poPpfOV/mgCGc8/vHbO8/fRmeIpflkeCFW67ae7adKwRdAAsFGdm7I/3u5LckuShJN1JTk5ybpKRXfz3Jzm7lPK4uRwIa1dK+YE0g9JJ8+vjN5M8mGYQ/YJxZRcleX+S7x5Xza1J7k6yJs0A7ch+97uSfL6U8rRa6x3TtOPlaQZU239FHEhyQ5ItaQYlzszogNjiJIfmrpqhUsqz0wxQj9TxxSTfXWs95L+IUsr5ST7cev72tn0jybY0wRqXZnQw+EeSnF9KeUqth+TP+lSSE9N8ziOh1Dtazz+Re2f+qg5p9wVJPpJkRdvi3UluTLKz1d4NrbaMnGWM/xX3liT/L8371H7V6aeTTNQnvz7BssvG1XtXkvuS7Ekz6PqwjAZ+LE/ytlLKmlrr6yd/dYc4P83nuab1+OY0792yJA/P6GdzbpKPlFIuq7Xum6yy1mf+32kG1Eb0J7m21e6zkpye5jvyiST/axZtnbVSyquTjLwffUmuS7P/ODXJeW1FfzLJ1iS/Nk19b0jyqnGL70/zeS9KM4i6Ks3A5bLMeCbd2SmlnJ7k42k+lxFDSa5Psj3NAOzI6+tN8vIkF5ZSvr/WOqMQ31LKDyf5+9bDwVbd29J8thdndH/73UnemeQFnb6eCRyJvj9XRgI8RuxL8z3amWQ4zX7r/DTHqiR5UpL/KaVcWWtdCJOULkuzv7uy9fiBNMemmuZzHjn2rkjyr6WUR9Rar5uqwvk6Ls1zn2wvvzvNcao/zWc3sj9bnuQvSymLaq1/OkVdZ2aezllKKf8ryR+PW/xgkpvS7IMuTvNZvSzNezqrQKNvdX0ZvZpgcSYfLFucpUl2jCk/83on78qLWs85Ub211tyQr6am5vxcYZ7sBar/wOjp6aIlk/+YObKuv+/I/5i5d9d96eruTVd3Twb69mTH5huzY/ONeeDO/8lFj35xeno7/jeAOdDXtptfPEE64THr6tjy86m/9TxTt2k01q2v7hd0cJQs5GPZiD3Zma50pyvd6U9ftuWBbMsDuSe35eH18ekt42O3OZL62v6dXzxFVqUlZWl25cjthzh29A3sOXh/ce/KScst6V3Z9KGjMJXB9Xf9R+7ddvUhy1cuPSmXnfmD6e7qnWArjoRj4TjGwrZl8+io/kjmgIk06wayZfPwnDzvf39kf77x9eZn3mc/b/mYdavXdmXpspL9+2puu3kgT3vWoX3woZ3D2balacvWOWrTsUjQAbCQXJ3kb5L8V631kFylpUn9/EtJXpFm//XwJL+XZrBtPry79fcdSV5Taz2Yc66Usm5c2d/J2ICDzyX5uVrr19u22Zjkt5L8bGvR2Un+vpTypMmuTi6lPDfN1Y4jtqaZfuC9tdZd48qelSbbwM/N6NVN/Hw/leRtGR08+2Caq0kPGYQupaxMc/Xvma1F25P8epL3tGcyKE2a/Z9P8rtpBkyuTJM94kXt9dVaX9wq/9qMTrHw9VrrMzt9PVN4fUYDDh5M8579+/jPoRVM8qQ0GQ7GnE3UWt+b5L2lmb6gPQPEi6cLJGnTnyYg4J+TfGyiq2BbGRnekNHAht8ppfy/WutXZ/gc700TcPAvSV7d/t0qpaxO8kdJfqK16Jwkv5Lme3WIVjaPv8/oAF1NM/D4+lrrzrZyT0ryl2kG6N88w3Z24tI078v+NMEEb2/vq6WUK9O8vyOD868opfxlrfXOiSprXbndHnBwW5rv60drbWJsW33ixWm+ly9IMueDyqWU7jTvc3vAwV8l+fX27CitjBNvS5OpImmyAvxekv9vBk+zIc2V70Npvg9/OO4zPDdN33lMa9GPlFL+rNb6uU5e0wSORN+fSzeneb/+M8n1tdYx/z2UUtamGQR+TZqB61PTfAd+4Ai3cyK/lebz/lqSX661fmpkRauv/VyaQe7uNPvoP07y9Mkqm8fj0nz2ye9Kk9nlQJJXJ/nLWuuBVr1dSZ6d5rs0kgf7zaWUz0/T1+b8nKWU8uiMfW/vS3P8/PeRPldKWZpmv/S6JC/MPOyDjmXDbTEYXZn8x4nu1rqhGcZsDM243p5J6707t2ZXduSknJ515YRD1rMwDA31H7w/1Y/V3d3NuqHB/knLzLX1J1+cVevOyuoNZ6d3UfMj2IF9O3L/7Z/Lvbd+Jru23ZYbvvzeXPK4nzxibeJQc7G/mA8jz9M9RZva23uk2sWhFvKxbGM2ZW02Zk02ZFFpYtcP1H25O7fkztyUndmaa3NVrhwTk8+RNtPPuuvgZ338Xo3JxIaGR89vuqY4HxpZ117+SOnpWpxFPcszXIcOTqmwculJueDUZ2b5kqM+Q+NxbSEfxzg2HNg3OrXB4iWTZ1Ba0lq3f9/hD/BvfmAov/urzbQg3/b0JXnCU8YGzHR3lzz6CYvzqY8eyD/97Z686GUrsnTZ2AsJ3v3noz+J7d17/AYduLwCWCjeXWu9stb6lol+vE+SWusDtdZfS/KjbYt/qpRJ8lYevpVJfqvW+lPtAQettmyvtZmss3XVfPsg3yeTPK094KC1zZZa68+lCVAY8dgkPz3Rk7cGsd7ZtuiOJI+ptb5t/MBOq/7ba61vSjPAesPMXuKY5/vtNINkI2duf53kOVNc9f7GjA7m3pPkEbXWPx8/dUKtdV+rXc9Jc1Vwkryw03muD1dr4PxZbYt+tNb6/okCP2qt/bXWj9dafzrJj89Dc55Ra/2RWuu/TJZ2u9b6hSRPS/JfrUXdSf73LJ5jQ5I/r7U+d/x3q9b6UK31pUk+1rb4JzK5n0pyRdvj/6/W+qr2gcFWvZ9JM1B8S5qBvvmyLs1Vvs+otb55fF9tDRh+V5oMCEkz8PdjE1VUmjygf9K26O4kT6q1fmQk4KBVZ3+t9R1pgowGMz+v76VJHt/2+A211p8cPx1LrfUbSZ6RJiPCiJeXUi6dwXMsT5Na/4W11t+Y4DO8Jc33pH1Ac6q+MVtHou/PlU8kOb/W+vpa67XjAw6SpNa6o7Wfe2aaQfMk+b5SykLI6bghyZeSPLE94CBJaq1Dtda3Jnlt2+KnlVLOmKiieT4uzWef3JgmSOp5rfOMg5c81FqHa60fSPLtSWviyaav/dkU9c3XOcufZvT/s+1JnlJr/UB7n6u17q+1/lGSH269po72QaWUr0x266Q+ptZX9+fWXJee9Oa8XHa0m8Mx6uxLnp0Nmy49GHCQJEuWrc1ZF39Pzrn0OUmSnVtuzo7NNx2tJgLf4s4vl+eEcsrBgIMkWVKW5bxyWc5v/Zu4PZuzrT5wtJoIHCfOP/UZecql/ztPveyVeeplr8qlZ/5ABob250s3vzs33vuRo9084Biyb+9wfuUnt2b71uGcfGp3XvPGiWfSfOnPr0x3d5PF4OdfvDXXXdOfgf6arZuH8va37Mp73r4nPa04ra7jeLYpQQfAglBr3TN9qYNl/yHJ51sPlyf5znlpVJPW+XemLdXMqTyyP92f5CV16vTmr01yTdvjXyoTT3z4C8nBSaqGk/xIrfW26RpTax1sH1CZTimluzU39G+0LX5drfUnpsjAcFLGDsK/ZLqr+2utH85o9ojk6M1FvTGjUwokyWdmslGtdYoZmzoz037f+hx+pW3Rs1tXKM/EHZl+ioM3td0/u5SyaZJy7VcrX5VDU4AfVJvpOH52svVz6E211s9O0Y5b0kx9MuJJkxR9esZmFnh5rfW+Ker9TKYelDwcv9R2/9qM/W6Ob0d/mu/iSMBFGbf9VP6u1vqPU9S9I02GhRGTvXezdoT6/pxoBU7V6Usmrb448p6WNGn1j7bhNMFVU73nf5IczD1YkjxhknLzfVyazz75nlrrf05R941JfrNt0WNb2VImKjvn5yytQLz2YLz/U2u9eYp6P5Dk72bajuNFV1siv+FMftgeaq3rnmHiv+4Z1ztyJfHYem/INRnKYM7OxVksXfmC1t09mhJ8aHjyKz+Hhpp13T0LI4X4SWc+LouXNT+ObX/wG0e5Nce3w91fzJfRq/4mb1N7e49UuzjUQj2WTefUnJ0laVL5b8n905RmPs30sx4++FlLQ89Y3V2j5zfDU5wPjaxrL3809HQvzslrL8mjz/vx9HQtzp2br8qDO2d9LRZz5Fg9jrFwLFk2OkzSd2Dyn+MOtNaNzzgwG30Han7lZdvyja8PZO36rrztPRuydt3EPzteesXi/Prr16anJ7n6i/350edszqPPuzdPf9T9+fM/2pXzL+rNc36oCRBfuer4HXo/fl85cKz7Qtv9R8/Tc/zVDAea2weV/qVOkrp9ROuKxfbB2oelmaN5vBe23f9QrfWqGbRlVlpTH/xbmiurk2YQ6Rdqrf9nmk1/OKMD91fXWj8+VeE2f9N2/2kzbujcGj9h4RUTllpgWoNP21oPVyS5aIabvqM1MD2Vz2Y0C0UyQX8spZw/bvmfTjcQW2v9WJJvzrCdnXrbDMq0X9090XctSb6n7f4DSf51BvXOedBB68r49ja+ZbLgnxG11rszOtCdJN83w6ebSfvb37tzW9NLHFGH0fePliNxfJqNj7cG1CfVylJwTduiyb4n831cms8++dYZlHlXkvaAgh+cYd3TmUmfaJ+KY3fGBulN5k+mLzKxWusjJrt1WudC0D5n6FRzePa1TgWmmmN0bL2jMyz1HXIaMaq/9Zzt9W6vm7Ml92Z5VmVTzshgHRxzq22H35Flw4cmVOEIWbRk1cH7/QcOSeByyLpFi1dNWuZIKqVk5ZrTkiQH9m4/yq05vrXPnz7VPOkj6xaXyecknksjzzN1m9rngT8y7eJQC/FYNhOllKxKMwvl/uydpjTzaex+aLLElcmBI7wf4tixuHfFwft9A7snLXegtW5x78p5b9NMLFm0KiesuSBJcu+2q49ya45fx+pxjIXjhBNHB/23PDj50MzIuo0ndDbMPdBf84qf3ZYvfr4vK1eV/PnfbsiZ50wdiPd9z1+ef/jQifnBFy7PeRf05qRN3bn0ikX55V9dnb/+lxPS39f8VH7aWcdv0Mvx+8qBBauUsjHNVccPT7IpzVWVi8cVa78i+dR5asqnpyvQSkF9ctui/5hh3f8+7vHjklzXVu+JSc5vW/9PM6x3xkopG9KkLR8ZADmQJq31+yff6qBva7v/0Vk87dfa7m8qpWya6mry+VBr3VlKuSWjfejvSik/2RogP2pKKVekubr4ojTTBqxMDpmkbHnb/VPTXAU/nWnnO6+17iulbE+Thj1J1kxQ7DHjHn9oBs+dNH3swhmWna07aq33zqDcPW3310xSpv31fWomAUe11ptLKXcnOW0GbZipx417PNN9yr9lNPvIhlLKeVNdJZ1kIE3K/em0v3clyerM8Rzy89j351wpZVWa49PlSc5I09Ylad6bEae03Z+v49NsTLsPaJnye3IEjkvz2Sc311qnnTagtS/8ZEaDkMbv9w4xh+cs7c/1yZlkhqi1frGUsjWj++7j3vKM/uC5N7vGPB5Ra82+VmzJ8sxswHhRWZzeuigD6c+e7Mr6nDRhuT3ZdUi9B1qJaPZmVz6Zf5v0OQ5kXz6ZDyRJLsojsylnzqhtzK2lKzem2bXU7Nv1YJatOOGQMrUOZ/+eZrezbOWh6zm+LS+j3/89deeE+5laa/a1ZiVaXlYfoXatztbcl70Tz2qVJNnTalNvFmeRrCxHzUI8lnFsWd41ul/ZM7xzzOMRtdbsG37okPKQJMuXjP57sefAljGPR9Ras69v2yHlj7aRAIj9/TumKcl8cRzjcJ15Tk9KSWpNbr1pcMJAgOHhmjtua67ROvu82WfsGRysefUvbs9nPnEgy5aXvPXdG3L+xTO7puWch/Xm11838RQMN1zXZIB5+JULIyPe0SDoAFgwWgP4b0qTOWA2+6c189KgZNqU0Rk7kJCMHVSfVGvg+64kp09Sz/hB2i/PpN5ZWJkm3fN5rcc7kzyn1jptoEVL+4TE31tKeXiH7diY5IgGHbS8McnbW/fPTPLR1uDxh9NMt3DVNAO2c6aU8uwkb8jsB+bXzLDcTCfU3JvRgatlE6xvn5v+vlrrTC+jm8/B4dm8thETvbakGUAeMZvsDN/I3AYdtO8LHqi1bp7hdl+foJ6p+vC2WuvkeRJHjb9MabL3b9aOQN+fM6WU9Ulel+THklmFy6+ZlwbNzlx9T+b7uDSfffK66YscdG1Ggw4eNlmheThnaX+u2ew3r03y7bMo/y2tp/RmVV2bXdmRbXkwJ4yJAWo8lO0ZTNPV1mXmA8Zrc0I2555sz4M5Y4KucaDuz97WD1yzqZeFpadnSVasOTV7dt6dnVtuzoZNlx5SZveOuzM02MQFrd44/hT+6Ki1ZvfOu5MkS5atO8qtOb71lN6sKuuzq27L9uEHcmL36YeUeahuHd0PdU38g/lcW9t1Yu4c+mb21IfSV/dPeGXz9uH7j2ibmNixeiyrtWZXmn8Rl46JF+ZI6ym9WdW1PruGt2Xb0P05seeMQ8o8NDy6H1rfffIh6zm+9XQvzqplm7Jr333Ztvu2nLjm0H/ZH9p3TwaHmpll168860g3cVL7+3cmOfpTPhzPjtXjGAvH8hVdueiy3lz/tYFc9dkDedqzDj1vvfbq/uzZ1WQVePQTxl/3MbXh4ZrffPn2fOLD+7NkScmb/2p9Hv6I2dUxkVtvGsjNNzT9+pnPmbOfT485gg6ABaE1l/FH0tkAzeEfFSY2eU7VUePD2mZzBfCWjAYdjK9n/K+FMx14nKl1457jzbMIOEiS9W33L0znV7IflZD6Wus7SilnJXl1Rq9QPi3JT7ZuKaXclyYjxTtrrXM9uJbWc/xukummspjMTPt9Xwd1lwmWtffRbROsn8xsys5WJ69tMmva7u+cxXZzHT7f/j7Pdn8yWT0T6fS9m6hvzL6SI9P350Qp5ewk/53R/fVsHNG2TmKu9gHzfVyazz7Z6T5rwu/RPJ2zLMR97DHppJyeXdmRB3JXzq4XHjKwdmduSpKszNosLzNPBXtSTsvm3JNteTC7686sLGvGrL+rVe+iLMnath+4NpUzp8xacGu9Prfnm1mSZXli+a4Zt4f5s/GUy7Nn593Zcu/VOf387xgz5UKS3HtrM8vLitWnTJgJYT7UWlPK5Lu7B+68Kn37mlOStSdecETaxORO6j4zuwa35f6h23N2z6WH7ocGm/jWlWVdlncdmavw1nedlEVZkv4cyJ2D38zDeq8cs3738I5sG27iFE/uPvOItInJLbRjWTL9fuje3HYwu8+GSa4+5cg5uees7OrflvsHb885vZdlcdfYwY87Bq5PkqzqWi/TARM6ee0l2bXvvty//dqcc9KTD5lC4Y4HmxnkVi09+YhlOhiuw+kqk6dR33tgWzbvvCFJsnZ5J/++M1cW4nGMY8uznrMs13/toXzoA/vyU7+0KhtPHJsQ9T1vbzJlXHhp77RTIrSrteZ3Xr0jH/q3/eldlPzBX67Pox5/+Bm+BvprXv8bzf9jT3jKkpx/0fEb+NTZZBcAc6iUsjzJ+zP64/1Akvcm+eEkl6YZ6FhSay0jtyS/Nd/tqnVGE+qOHzzon8VTtA+wjD+6jX88bZrlWdqcsWmsf7OU8tJZbD9Xly4cteNQrfXXkjwyyfsydg7vEZuS/EySL5VS/qmUcWeyh6mU8pyMHXS9N8lvJ/mOJGenyUbRM67f3zmXbZil9rOlTvs502vfpxzO+7xgc+IeS32/lNKVZhqBkV8sapqpLF6SZoqFDUmWjmvrj09Q1beC+T4uzadOv0uHBAjM4zmLfewcOSVnZ0mWZSiDuSafO5gufLAO5Ob69WxJMyvPubn4kG0/Vv85H6v/nFvr9Yes25hNB+eq/nq+kIdqE+8xXIdyZ70pd7WSy5yTi6b8MZSF76QzH5vFS9dmaLAv1//PX2ff7geTJIODB3L79f+Vbfc3yVPOuPBZh2z72X9/ZT7776/MnTd8ZMK6B/v3ZaBv78HbweUDB8YsHx4eO8vTbdf9W2699t/y0LbbMzQ0mhSmb//O3PGND+bWa5upO1ZvOCfrBB0cdad2n5slWZ6hDObq/k9mTyuF+WAdyE0DX83m4SYrxXk9lx+y7UcPvC8fPfC+3DowPolVY6D2pb8eOHgbMZiBMcuHx/0r21W6c3ZPk7njzqEbcsfgNzPcmk1s5/CWfG3g00lq1pSN2di9EGaHOr4txGPZjbkmN9ZrsrNuzVDbTHQH6r7cXK/NjbkmSbI2G7OhuHL+aDu152FZUpZnKAP5at8nsmd4Z5LWfqj/K9k8dFeS5NzeKw7Z9iN735OP7H1Pbum/ZsK6O90PJclQHRxXpulLwxkes3xwRgnQmE+nbnhElixanaHh/nz11n/Inv3NdQ6DQ3256d6PZvNDzeD+uZueesi2H7n6t/ORq387t9z/yQnrHhjcn/7BfQdvIwaH+sYsHx436+UN93w4N9zz4ezcc3eGhgfb6juQe7ddky/f/DcZroPp7lqUM0547OG+BRyGhXgcS0b2QX0Hb8Np9lPNPmh0+WAdPGRbjqwffOGKnHxqd/buqfnln9iaW29qjgt79wznza/bmU98eH+S5BdfeWjg3BVn3JMrzrgnf/HHh04r9ge//VA+8I/70tOT/P6frc8TnjK7n0/f8Bs78tUv9mX/vlbfGa756hf78lM/siVfuao/a9d35f+8bs0sX+23FpkOgIXgxzM6x/FAkqfXWj81zTYzD4OcXzvHPV6ZiQewJ9J+Wcv4esZfPb16FvXOxP40A3wfTjOHfFeSd5RSemutfzGD7XdmNBX/y2utfzyHbTtiaq1fTfKiUkpvkkelmVf+SUmekrF97HlJTi2lPKnWcf/1dO432u5/KU2/n3yS1cbR7PftmT9m045jZRK1nUlObN1fM4vtpsso0Ek7RhzO+7xzokILxLHU978rySPaHr+o1vp302wzn23tnr7IvJnv49J86vS7NFG/nK9zll0ZzSbxrbiPPWK6S3ceXh+fr+bT2Z2duSofSXftyVBGfzg6N5dkfZndVZillFxWH5uv5FPZn735Uv473bUnwxlKTZPW8ZScnVPK2XP6ejjyurt7c+GjX5zrvvD27H3o3nz1v/8w3T1LMjTYlyb2rOSMC5+ZtSdMOgPLpK7+1FvSt//QJEk3fuV9Yx5f8vifzpoN5xx8PDTYl813fyX33/65JCU9vUtSaz04zUOSrFp/di545I/Ouk3Mve7Sk8sXfVu+0v/x7K7b84X+/0xPejOYwaS1vzi35/KOUppf1fehHDhkpqHk2oHPjnn8iN7vyLruE8csO63nYdldd+TeoVty8+BXc8vgNelK18H949KyIpcueuKs28TcW4jHsqEM5v7cmbtzS5Kkp/ampo5p05psyGV5XCcvmTnWXXpyxeJvz5cPfDS7h7fn8/v//ZD90Hm9V2RDz6ZZ1/2F/f+ZA/XQ/dDX+8Ymz3zkkmdkXffYPnr7wHW5bYKgqs1Dd2XzvrsOPt7Uc04uWfyEWbeNudPd1Zsrznp+vnzL32b3/vvz+Rv+PD1dizM43J+Dfejkp2bDqnOmrmgCX7jx7TnQf+i/Wl+/41/GPH7kuT+WdSvPPPh4eHgg92z/Wu7a8sUkJT3dTYz44NDo+dCinhV5+FnPzZJF/k06mhbicSxJ7siNuX2CGVW35L5saZv99+SckYvzqFm1jbm1ZEnJH79jfX7mBVvzzesG8tynP5gVK0v27a0ZHk5KSX7hlavyuCfPPGjg/nsH83fvav2MVZLf+7Ud+b1fmzyB7ce+fOgx8h/fszf/+J7mGLhyVcn+/TWDrTi5Tad25y3v2pCTTzm+h92P71cPLBTPbLv/9zP48T6Z2znUD8f49NLnJLl/uo1aV8+2T3o2vp7xdZyftMJA50itdVcp5RlJPphmoL0k+fNW4MFbp9n8gYwGHZw4VcFjQWse8c+3bm8qpSxKM6f372Z06ojHJXl+kukGHKdVStmYsQOZr5pu0LWUsiJHd374B9vun1ZK6Z5hAMaxMvpyZ0b78mymC7lojtvRvi84vZTSU+uMQqzH/6c/16nv58Qx2Pfbj0+fnkHAQTLz41P71ewzzQU310EuszHvx6V5NJtJRtv3WQ9OsH6+zlkezGjQQaftpWVlWZPH1mfkjtyQrbk/fdmf3izO6qzN6Tkv60pnpy5LyrI8pn5H7siN2Zx7cyB7052erMyanJpzcmJxdfC3ihWrN+XKp/zv3H3zJ7LjwW+m78Cu9C5alhVrT8spZz8pazaed0Tbc9IZj03vouXZtf3O9O3fmcGBfam1ZvHSNVmx+tRsPPXyrD/5khRZNhaMlV1r87jF353bB6/P1uF701f3pTeLsrprfU7vvjDru49O+vmLeh+TdV0n5Z6hm7N7eEeGM5TlZVVO6DotZ/ZcnJ4y8/S0zK+Fdiw7NWdnURZnZ7blQPZlIM3A4+IszaqszUk5PSfklCmnYODIWtm9Lo9f+uzcPnBttgzd09oPLc7q7vU5o/eijgKfOL6sXHZSHn/hz+b2Bz+bLQ/dnL6BXentWZrVy07JGSc8JutXHtl/Rc468QlZvmRDtu++Pfv6dqR/cE+G63AW9SzPiiUnZOPq87Jp/eXp7V6wiR+PKwvtOMax5/yLFuX/fuTEvOttu/OZj+/P5geHsnptVy55+KK88KUr8pgnzu673p6AZ3Ag2bZlJkmux/rlX12dL33+QG69aTDbtw1l+fKunHF2T572rKX5oR9bkSVLnAcJOgAWgjPa7n9xusKl+S/28fPXnFm5Ns2VjiO/zjw+yWcnL37QZRk7RcGXx62/LsnujF7t+JQkn+i4lZOote4ppTwzyX8kGcmJ9iellEW11j+cYtPPJ7mkdX+uL2VoP+IflSN1rbU/yftLKZ9Lcn2S9a1V35lDgw7Gn6HMpM3jJ5ebtt+n6VtH85fkr7TdX5qmD189g+0eMz/NmXP/k+TRrfvfNpOgilLKeZn7AKj293lJkisz8/4xYihp5TddeI61vj+r41PLTC8RbM8esm7SUmNdOsNy8+GIHJfmyQWllFW11l3TFx2zz/rKBOvn65zlKxkNeJrRfrOUsjpN8AcTWFyW5PxcnvNz+Yy3+Y7y3GnL9JTenJtLcu7B06DDc065OOdMkFaUo2/RkpU559LnJJc+Z8bbPPHZb5xy/aOe/qsdtWXVujOyat0Z0xdkQVlcluaC3kemmdFtZp6+5IVTrn/Sku87vEYlOan7jJzUrT8dCxbSsWx1WZ/VB/8t5lixuGtpLlj86Fxw8F/d6T1j+Y9Nuf7Jy36w4/acu+jynLvo8o6358hb3LsiF5z6zFxw6jOnL9zyjCt+c8r1T774lztqy/IlG3LWkg0560RZMI4VC+k4lvjf61i04YTuvPK1a/LK166Z8TZX3zlx4Mmm03omXTdTL/mZlXnJzyyUBNwLkzB4YCGY7eUUz0xyynw0ZLZqrQfSDFaOeFGZWWj/i9vu9ye5aly9g0k+3rboJ0oph8wtPRdqrfvSXNXfPvnsH5RSXj3FZh9qu//EUspcDnq05+lbOof1zlqt9cEkn2tbNNElSePzCs6kzZ1cQvTSDraZS1/M2Dncf2S6DVoDYt89by2aW//Zdv+kJN8/g21+fh7aMf59nmme5PZfhr5Sa12oae+Ptb4/q/aWUi7KzAOx7my7f9kM6n5EDg3aOGKO5HFpHvQmmfaXi1LKZcmYXy0mymIwX+cs7c91WasvTef5EUQOAAAAwAIg6ABYCO5ru//kqQqWUpYl+eP5bc6svaPt/qVJXjJV4dbV0T/btuifaq07Jyj65rb7pyR5XWfNm16tdX+SZyf5r7bFry+l/MYkm/x7khtb97uSvKOUOcvF2Z7C+5wZBnHMWAf1rWi7v32C9TszdpB4Jvl27xv3eLp+/7Qkz5tBvfOm1ro7yT+3Lfr5Usp0A6CvzVEOHJmFjyatCUobf1RKmTTfZCnlSZmHoINWsMDfty36qVLKBVNtU0r50SRXtC16+1y3aw4da31/NsenriR/Nou62zPcfFdrGonJ6i5JXj+LuufLm9vuz+txaR78Zill+TRlfr/t/u4k/zRBmfk6Z/mnjA1i+/3JCrbqXpFksmM0AAAAABxRgg6AhaA9PfNzSynfM1GhUsr6NFcjL7RUwv+Y5Ia2x29rTVlwiFLKmUk+mGTk6tC+TDKQ1Jon+j/aFr28lPLGqa4sLaUsKqW8tPU8s1Jr7UvyA0k+0Lb4t0spvzNB2eEkv5KkthY9KcmHSymbpnueUspFpZQ/LaW8YpIi7ems1yX58Rk0fzaeXEr5UCnlGaWU7qkKllK+N00K8RH/Pb5MKwX/NW2Lfq6UMuWkUrXWu5Lc2rboD1r9e6I2PCXJv+QoTTUxzu+nmU4kSZYl+c/JBuZLKT+X5H8doXYdtlprTfJLbYtOS/KZUsrT2wNVWt+xn0wToNOTZMs8NOf3MxrIsijN+zzhZImllKcn+cu2RTcned88tGlOHIN9v/349OhSys9OVKg1uPzejN1fTOdf2u6vydgB/fa6e5P8RZKnz6LueXGkj0tz7Iwk/1xKOSQPXimlu5Tyh2myEox4yyQZQ+blnKU19cNb2xZ9T+u9PeT/tVLKqiT/msRklQAAAAAsCNJxAgvB25O8Ks0V5V1J/q2U8rdpBjYeTLI2zaD2TyRZn2Ye7P/KDNK7Hwm11r5SyovSpOFfnGYe9g+WUt6f5P1J7kkzoPTtSV6WsVfOv6rW+o0pqn9xki8lOaf1+BVJnl9K+bs00zpsS3Ml+VlpUno/O837dcWhVc3otfSXUp6X5krrkVTUv15K6a21vnpc2Q+VUn4to0ETT01yW+t1fyLJXUn2JVmV5orYy1tlRq7a/q1J2nBjKeXLGZ389J2llF9NM5ja31b0T2qtncwnXtIMLD0zyYOllA+nueL49iQPpUmdfXaaaQGek9EAvVvTDCpO5L1JHtu6/4wk95dSrknTV0cCM66rtf562zZ/mORtrfsXJbm2lPK2NJ9rf5oBsuekSfNf0gSrXJKjm179ulLK76XJYJA0mT2uL6W8PclnkuxJ8969MMnTWmX+LskLjnBTO9Lq029M8srWonPSTDtyXynlljR946Ikq1vr/y5NEMbIdCl9c9SOG1tBOSMDkOck+Xop5V1p0tvvSLIpyfcl+aGMDsr3J3lRa9qXhexY6vv/N83V/Ke1Hr+tlPKMNFel35NkZZJHpzk+nZamP7wnM5gSotZ6UynlX5KMTIr60tZUNe9McluawJ4rW3Wfk+YK++tz9IMPjuhxaY58LM0x6Jlp9ll/mWa/P5DmmPTSNO/1iOuS/O4kdc3nOctvpen7F7YevyLJU1vf/RvS7IMeleSn0wQcbEnytSTfMYO6AQAAAGDeCDoAjrpa6+ZSyovTZAzoSfMj/oszOpDXbm+SH07ymCPXwunVWr/Sym7wgTQDkiXNQNIPTrZJklfXWt8yTb07SimPb9U7Mk/46UlePelGh6nWOlhK+eEkf5vRQZJXlVIW1VpfPq7sG0opD6YZQFySJujiR3L4ASE/mWaQaOQK6HNbt3YfOMznSJITM3lfa3dXku+pte6bZP1fJvneJN/Zerwmh17xvGbc479IMzA/0kdOTnJIVomWr6YZyL9mmnbOu1rrb5VSTkryM61Fa9MMwL1qguJvTxPAckwEHSRJrfVVpZTdadKWL2ot3tS6tXtnkl9I8u62ZQ/NYTv+tHX1+JvS7E+WJ/nF1m0iu5N8f631i3PVhnl0zPT9VlDZD6UJ9ljWWvx9rdt4A2mmzhnKDIIOWn4hycMzun97Yus23pY0g/eTff5HzNE4Ls2Be9NkEPlAmuCQyQIKkibA7Rmt7D+HmM9zllrrgVb2kk9lNKjjEa3bRHW/IMmLZlI3AAAAAMwn0ysAC0Kt9f1prtS7bpIiQ2muOL6y1vqhI9awWai1fjLJxUneldHU6IcUS5Oi/3G11jfOsN7Naa6afFmSm6YpfleSN2Rs+vJZa00Z8KIkf9O2+FdKKW9tTzXfKvvXadJH/1mmH3Tdk+aKzxenGUyd7PmvSfNevibNFfRbMjbLweG4NslvJrkqo1MFTGZLkjcmubTWesNkhWqtg0m+K80A0L+myZqwN6NZDibapiZ5fqstuyYptiPN5/m4WuvOadp6xNRafzZN/7hnkiL3JvnpWutPH7lWzZ1a6+8muSzNXOzfSDOgvzfN9+9vknxbrfVlrYwCJ7ZtOqdTLdRa/zDNYOXHkwxPUuxAq00X1Vo/PpfPP1+Otb5fa70qTSaTz09R7AtJnlhrfecs634gzf79nzLx/mIozUD55bXWr0yw/qg4Gselw1Vr/ViarBSfmaRIX5oAukfWWu+fpq55O2eptd6bJsjgLzJ59pTPJHl06zUBAAAAwFFXmt99ARaG1oD2lWlS669PM9h3f5LPtgZnjgmllKVJnpwmvfS6NIPt9yX5dGuw5nDqPifNwMkJaVJ7701yd5Kv11qnG/yZV6WU7jSf30VpPr+ladr3QJrU0NfXWqcb6D9iWp/TyFXGJ6S5kvlAkq1pBpOuaQUUzHc7VqTpLw9L855tSXJHkk8tpPdrvNZc409IEyCyNk27b07ymVrrZIPk3zJKKT1Jtqf5HibJ0+drELCUsjFNH9nUer7tafrIp6fIwLHgHWt9v5RyYZLHp9lf7E9zfPpirfX2Oaj75DTT8JySZtD6njTfpSkHwBeChXhcKqW8O6PZB/6m1vqStnXnpAno2ZTmvb4zycdqrZMFwUz2HPN6zlJKWZ0mK8gZSbrTnEf8T611XgM4nt71PP8gclgOfO+jj3YTOMYt/cjXjnYTOMbVvjmZ9YzjWNeyZdMXgqk87Myj3QKOccPXTDUbL0zvTXdcdbSbwDHu8tPvLtOXGkvQAQBAB0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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 796,\n \"width\": 1038\n },\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"count = len(descriptions)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(20, 14))\\n\",\n \"plt.imshow(similarity, vmin=0.1, vmax=0.3)\\n\",\n \"# plt.colorbar()\\n\",\n \"plt.yticks(range(count), texts, fontsize=18)\\n\",\n \"plt.xticks([])\\n\",\n \"for i, image in enumerate(original_images):\\n\",\n \" plt.imshow(image, extent=(i - 0.5, i + 0.5, -1.6, -0.6), origin=\\\"lower\\\")\\n\",\n \"for x in range(similarity.shape[1]):\\n\",\n \" for y in range(similarity.shape[0]):\\n\",\n \" plt.text(x, y, f\\\"{similarity[y, x]:.2f}\\\", ha=\\\"center\\\", va=\\\"center\\\", size=12)\\n\",\n \"\\n\",\n \"for side in [\\\"left\\\", \\\"top\\\", \\\"right\\\", \\\"bottom\\\"]:\\n\",\n \" plt.gca().spines[side].set_visible(False)\\n\",\n \"\\n\",\n \"plt.xlim([-0.5, count - 0.5])\\n\",\n \"plt.ylim([count + 0.5, -2])\\n\",\n \"\\n\",\n \"plt.title(\\\"Cosine similarity between text and image features\\\", size=20)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"alePijoXy6AH\"\n },\n \"source\": [\n \"# Zero-Shot Image Classification\\n\",\n \"\\n\",\n \"You can classify images using the cosine similarity (times 100) as the logits to the softmax operation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 102,\n \"referenced_widgets\": [\n \"1369964d45004b5e95a058910b2a33e6\",\n \"12e23e2819094ee0a079d4eb77cfc4f9\",\n \"7a5f52e56ede4ac3abe37a3ece007dc9\",\n \"ce8b0faa1a1340b5a504d7b3546b3ccb\",\n \"5e6adc4592124a4581b85f4c1f3bab4d\",\n \"4a61c10fc00c4f04bb00b82e942da210\",\n \"b597cd6f6cd443aba4bf4491ac7f957e\",\n \"161969cae25a49f38aacd1568d3cac6c\"\n ]\n },\n \"id\": \"Nqu4GlfPfr-p\",\n \"outputId\": \"ca7a0e3c-e267-4e6e-8a1b-bbab3c0a2462\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Files already downloaded and verified\\n\"\n ]\n }\n ],\n \"source\": [\n \"from torchvision.datasets import CIFAR100\\n\",\n \"\\n\",\n \"cifar100 = CIFAR100(os.path.expanduser(\\\"~/.cache\\\"), transform=preprocess, download=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"id\": \"C4S__zCGy2MT\"\n },\n \"outputs\": [],\n \"source\": [\n \"text_descriptions = [f\\\"This is a photo of a {label}\\\" for label in cifar100.classes]\\n\",\n \"text_tokens = tokenizer.tokenize(text_descriptions)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"id\": \"c4z1fm9vCpSR\"\n },\n \"outputs\": [],\n \"source\": [\n \"with torch.no_grad():\\n\",\n \" text_features = model.encode_text(text_tokens).float()\\n\",\n \" text_features /= text_features.norm(dim=-1, keepdim=True)\\n\",\n \"\\n\",\n \"text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)\\n\",\n \"top_probs, top_labels = text_probs.cpu().topk(5, dim=-1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 931\n },\n \"id\": \"T6Ju_6IBE2Iz\",\n \"outputId\": \"e1a155dc-474d-409c-e03d-d41b804648c3\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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"content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n },\n \"accelerator\": \"GPU\",\n \"gpuClass\": \"standard\"\n },\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"!pip install open_clip_torch transformers\"\n ],\n \"metadata\": {\n \"id\": \"JvaEkx8Cyvhg\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"id\": \"vE4lFFkKyotX\"\n },\n \"outputs\": [],\n \"source\": [\n \"import open_clip\\n\",\n \"import torch\\n\",\n \"\\n\",\n \"model, _, transform = open_clip.create_model_and_transforms(\\n\",\n \" model_name=\\\"coca_ViT-L-14\\\",\\n\",\n \" pretrained=\\\"mscoco_finetuned_laion2B-s13B-b90k\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"!wget https://i.imgur.com/8H7XCH0.jpg -O cat.jpg\"\n ],\n \"metadata\": {\n \"id\": \"oOaE1AmDyth_\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from IPython.display import Image\\n\",\n \"Image('cat.jpg')\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 407\n },\n \"id\": \"Y9Q6bhVA2L01\",\n \"outputId\": \"1b920080-e8cd-4d2f-fb23-30e6f1b612f9\"\n },\n \"execution_count\": 19,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"image/jpeg\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 19\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from PIL import Image\\n\",\n \"im = Image.open(\\\"cat.jpg\\\").convert(\\\"RGB\\\")\\n\",\n \"im = transform(im).unsqueeze(0)\\n\",\n \"\\n\",\n \"with torch.no_grad(), torch.cuda.amp.autocast():\\n\",\n \" generated = model.generate(im)\\n\",\n \"\\n\",\n \"print(open_clip.decode(generated[0]).split(\\\"\\\")[0].replace(\\\"\\\", \\\"\\\"))\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"byZKXMGzyr5Y\",\n \"outputId\": \"122eb099-6704-4e3c-fa7c-a05dd87ce64f\"\n },\n \"execution_count\": 22,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"an orange and white cat on top of a turtle . \\n\"\n ]\n }\n ]\n }\n ]\n}"
},
{
"path": "docs/open_clip/clip_conceptual_captions.md",
"content": "## Additional training curves for CLIP on Conceptual Captions\n\n# Zero shot accuracy\n\n\n# Training loss curve\n\n\n# Validation loss curve\n\n\n# Validation recall\n"
},
{
"path": "eval_few_shot.sh",
"content": "#!/bin/bash\n\nexport CUDA_VISIBLE_DEVICES=0\nexport DATA_ROOT=\"/local/scratch/imageomics/projects/open_clip/data/rare_species\"\nexport PRETRAINED=\"openai\"\n#\"laion400m_e32\" \"openai\"\n#\"/local/scratch/imageomics/projects/open_clip/model/10m_random/epoch_100.pt\"\n#\"/local/scratch/imageomics/projects/open_clip/model/inat_random/epoch_65.pt\"\n# export PRETRAINED=\"../storage/logs/2024_03_29-02_27_41-model_ViT-B-16-b_256-p_amp-few_shot/pickle.p\" #when TASK_TYPE=\"eval\"\n\nexport TASK_TYPE=\"all\"\n# export TASK_TYPE=\"eval\"\nexport LABEL_FILE=\"metadata.csv\"\nexport LOG_FILEPATH=\"../storage/logs\"\n\npython -m src.evaluation.few_shot \\\n --model \"ViT-B-16\" \\\n --batch-size 256 \\\n --data_root $DATA_ROOT \\\n --pretrained $PRETRAINED \\\n --label_filename $LABEL_FILE \\\n --log $LOG_FILEPATH \\\n --task_type $TASK_TYPE \\\n --nfold 5 \\\n --kshot_list 1 5 \\\n\n# conda activate bioclip-test\n# ./eval_few_shot.sh &> ../storage/fewshot_10m_random.txt\n"
},
{
"path": "eval_zero_shot.sh",
"content": "#!/bin/bash\n\nexport CUDA_VISIBLE_DEVICES=0\nexport DATA_ROOT=\"/local/scratch/imageomics/projects/open_clip/data/rare_species\"\nexport PRETRAINED=\"/local/scratch/imageomics/projects/open_clip/model/10m_random/epoch_100.pt\"\n# \"laion400m_e32\" \"openai\"\n#\"/local/scratch/imageomics/projects/open_clip/model/10m_random/epoch_100.pt\"\n#\"/local/scratch/imageomics/projects/open_clip/model/inat_random/epoch_65.pt\"\nexport TEXT_TYPE=\"taxon_com\"\n#\"com\", \"sci\", \"sci_com\", \"taxon_com\"\nexport LABEL_FILE=\"metadata.csv\"\nexport LOG_FILEPATH=\"../storage/logs\"\n\npython -m src.evaluation.zero_shot_iid \\\n --model \"ViT-B-16\" \\\n --batch-size 256 \\\n --data_root $DATA_ROOT \\\n --pretrained $PRETRAINED \\\n --label_filename $LABEL_FILE \\\n --log $LOG_FILEPATH \\\n --text_type $TEXT_TYPE \\\n\n\n# conda activate bioclip-test\n# ./eval_zero_shot.sh &> ../storage/zeroshot_10m_random.txt\n"
},
{
"path": "justfile",
"content": "lint: fmt\n\truff check src/imageomics scripts/\n\nfmt:\n\truff format src/imageomics scripts/\n\ntest: lint\n\tpytest src/imageomics/\n"
},
{
"path": "pyproject.toml",
"content": "[tool.isort]\nprofile = \"black\"\n\n[tool.ruff.lint]\nignore = [\"E501\"]\n"
},
{
"path": "requirements-training.txt",
"content": "absl-py==2.1.0\nbraceexpand==0.1.7\ncachetools==5.3.3\ncertifi==2024.2.2\ncharset-normalizer==3.3.2\nfilelock==3.13.1\nfsspec==2024.3.1\nftfy==6.2.0\ngoogle-auth==2.29.0\ngoogle-auth-oauthlib==1.0.0\ngrpcio==1.62.1\nhuggingface-hub==0.21.4\nidna==3.6\nJinja2==3.1.3\nMarkdown==3.6\nMarkupSafe==2.1.5\nmpmath==1.3.0\nnetworkx==3.2.1\nnumpy==1.26.4\nnvidia-cublas-cu12==12.1.3.1\nnvidia-cuda-cupti-cu12==12.1.105\nnvidia-cuda-nvrtc-cu12==12.1.105\nnvidia-cuda-runtime-cu12==12.1.105\nnvidia-cudnn-cu12==8.9.2.26\nnvidia-cufft-cu12==11.0.2.54\nnvidia-curand-cu12==10.3.2.106\nnvidia-cusolver-cu12==11.4.5.107\nnvidia-cusparse-cu12==12.1.0.106\nnvidia-nccl-cu12==2.19.3\nnvidia-nvjitlink-cu12==12.4.99\nnvidia-nvtx-cu12==12.1.105\noauthlib==3.2.2\npackaging==24.0\npandas==2.2.1\npillow==10.2.0\nprotobuf==5.26.0\npyasn1==0.5.1\npyasn1-modules==0.3.0\npython-dateutil==2.9.0.post0\npytz==2024.1\nPyYAML==6.0.1\nregex==2023.12.25\nrequests==2.31.0\nrequests-oauthlib==2.0.0\nrsa==4.9\nsafetensors==0.4.2\nsix==1.16.0\nsympy==1.12\ntensorboard==2.14.0\ntensorboard-data-server==0.7.2\ntimm==0.9.16\ntokenizers==0.15.2\ntorch==2.2.1\ntorchvision==0.17.1\ntqdm==4.66.2\ntransformers==4.39.1\ntriton==2.2.0\ntyping_extensions==4.10.0\ntzdata==2024.1\nurllib3==2.2.1\nwcwidth==0.2.13\nwebdataset==0.2.86\nWerkzeug==3.0.1\n"
},
{
"path": "requirements-training.yml",
"content": "name: bioclip-train\nchannels:\n - defaults\ndependencies:\n - _libgcc_mutex=0.1=main\n - _openmp_mutex=5.1=1_gnu\n - bzip2=1.0.8=h5eee18b_5\n - ca-certificates=2024.3.11=h06a4308_0\n - ld_impl_linux-64=2.38=h1181459_1\n - libffi=3.4.4=h6a678d5_0\n - libgcc-ng=11.2.0=h1234567_1\n - libgomp=11.2.0=h1234567_1\n - libstdcxx-ng=11.2.0=h1234567_1\n - libuuid=1.41.5=h5eee18b_0\n - ncurses=6.4=h6a678d5_0\n - openssl=3.0.13=h7f8727e_0\n - pip=23.3.1=py310h06a4308_0\n - python=3.10.13=h955ad1f_0\n - readline=8.2=h5eee18b_0\n - setuptools=68.2.2=py310h06a4308_0\n - sqlite=3.41.2=h5eee18b_0\n - tk=8.6.12=h1ccaba5_0\n - wheel=0.41.2=py310h06a4308_0\n - xz=5.4.6=h5eee18b_0\n - zlib=1.2.13=h5eee18b_0\n - pip:\n - absl-py==2.1.0\n - braceexpand==0.1.7\n - cachetools==5.3.3\n - certifi==2024.2.2\n - charset-normalizer==3.3.2\n - filelock==3.13.1\n - fsspec==2024.3.1\n - ftfy==6.2.0\n - google-auth==2.29.0\n - google-auth-oauthlib==1.0.0\n - grpcio==1.62.1\n - huggingface-hub==0.21.4\n - idna==3.6\n - jinja2==3.1.3\n - markdown==3.6\n - markupsafe==2.1.5\n - mpmath==1.3.0\n - networkx==3.2.1\n - numpy==1.26.4\n - nvidia-cublas-cu12==12.1.3.1\n - nvidia-cuda-cupti-cu12==12.1.105\n - nvidia-cuda-nvrtc-cu12==12.1.105\n - nvidia-cuda-runtime-cu12==12.1.105\n - nvidia-cudnn-cu12==8.9.2.26\n - nvidia-cufft-cu12==11.0.2.54\n - nvidia-curand-cu12==10.3.2.106\n - nvidia-cusolver-cu12==11.4.5.107\n - nvidia-cusparse-cu12==12.1.0.106\n - nvidia-nccl-cu12==2.19.3\n - nvidia-nvjitlink-cu12==12.4.99\n - nvidia-nvtx-cu12==12.1.105\n - oauthlib==3.2.2\n - packaging==24.0\n - pandas==2.2.1\n - pillow==10.2.0\n - protobuf==5.26.0\n - pyasn1==0.5.1\n - pyasn1-modules==0.3.0\n - python-dateutil==2.9.0.post0\n - pytz==2024.1\n - pyyaml==6.0.1\n - regex==2023.12.25\n - requests==2.31.0\n - requests-oauthlib==2.0.0\n - rsa==4.9\n - safetensors==0.4.2\n - six==1.16.0\n - sympy==1.12\n - tensorboard==2.14.0\n - tensorboard-data-server==0.7.2\n - timm==0.9.16\n - tokenizers==0.15.2\n - torch==2.2.1\n - torchvision==0.17.1\n - tqdm==4.66.2\n - transformers==4.39.1\n - triton==2.2.0\n - typing-extensions==4.10.0\n - tzdata==2024.1\n - urllib3==2.2.1\n - wcwidth==0.2.13\n - webdataset==0.2.86\n - werkzeug==3.0.1\n"
},
{
"path": "requirements-viz.txt",
"content": "pandas==2.1.2\nplotly=5.18.0\n"
},
{
"path": "requirements.txt",
"content": "certifi==2024.2.2\ncharset-normalizer==3.3.2\ncmake==3.28.4\nfilelock==3.13.1\nfsspec==2024.2.0\nftfy==6.1.1\nhuggingface-hub==0.21.3\nidna==3.6\nJinja2==3.1.3\nlit==18.1.2\nMarkupSafe==2.1.5\nmpmath==1.3.0\nnetworkx==3.2.1\nnumpy==1.26.4\nnvidia-cublas-cu11==11.10.3.66\nnvidia-cuda-cupti-cu11==11.7.101\nnvidia-cuda-nvrtc-cu11==11.7.99\nnvidia-cuda-runtime-cu11==11.7.99\nnvidia-cudnn-cu11==8.5.0.96\nnvidia-cufft-cu11==10.9.0.58\nnvidia-curand-cu11==10.2.10.91\nnvidia-cusolver-cu11==11.4.0.1\nnvidia-cusparse-cu11==11.7.4.91\nnvidia-nccl-cu11==2.14.3\nnvidia-nvtx-cu11==11.7.91\npackaging==23.2\npandas==2.0.2\npillow==10.2.0\npython-dateutil==2.9.0.post0\npytz==2024.1\nPyYAML==6.0.1\nregex==2023.12.25\nrequests==2.31.0\nsafetensors==0.4.2\nscipy==1.10.1\nsix==1.16.0\nsympy==1.12\ntokenizers==0.15.2\ntorch==2.0.1\ntorchvision==0.15.2\ntqdm==4.66.2\ntransformers==4.38.2\ntriton==2.0.0\ntyping_extensions==4.10.0\ntzdata==2024.1\nurllib3==2.2.1\nwcwidth==0.2.13\n"
},
{
"path": "requirements.yml",
"content": "name: bioclip-test\nchannels:\n - defaults\ndependencies:\n - _libgcc_mutex=0.1=main\n - _openmp_mutex=5.1=1_gnu\n - bzip2=1.0.8=h5eee18b_5\n - ca-certificates=2023.12.12=h06a4308_0\n - ld_impl_linux-64=2.38=h1181459_1\n - libffi=3.4.4=h6a678d5_0\n - libgcc-ng=11.2.0=h1234567_1\n - libgomp=11.2.0=h1234567_1\n - libstdcxx-ng=11.2.0=h1234567_1\n - libuuid=1.41.5=h5eee18b_0\n - ncurses=6.4=h6a678d5_0\n - openssl=3.0.13=h7f8727e_0\n - pip=23.3.1=py310h06a4308_0\n - python=3.10.13=h955ad1f_0\n - readline=8.2=h5eee18b_0\n - setuptools=68.2.2=py310h06a4308_0\n - sqlite=3.41.2=h5eee18b_0\n - tk=8.6.12=h1ccaba5_0\n - wheel=0.41.2=py310h06a4308_0\n - xz=5.4.6=h5eee18b_0\n - zlib=1.2.13=h5eee18b_0\n - pip:\n - certifi==2024.2.2\n - charset-normalizer==3.3.2\n - cmake==3.28.4\n - filelock==3.13.1\n - fsspec==2024.2.0\n - ftfy==6.1.1\n - huggingface-hub==0.21.3\n - idna==3.6\n - jinja2==3.1.3\n - lit==18.1.2\n - markupsafe==2.1.5\n - mpmath==1.3.0\n - networkx==3.2.1\n - numpy==1.26.4\n - nvidia-cublas-cu11==11.10.3.66\n - nvidia-cuda-cupti-cu11==11.7.101\n - nvidia-cuda-nvrtc-cu11==11.7.99\n - nvidia-cuda-runtime-cu11==11.7.99\n - nvidia-cudnn-cu11==8.5.0.96\n - nvidia-cufft-cu11==10.9.0.58\n - nvidia-curand-cu11==10.2.10.91\n - nvidia-cusolver-cu11==11.4.0.1\n - nvidia-cusparse-cu11==11.7.4.91\n - nvidia-nccl-cu11==2.14.3\n - nvidia-nvtx-cu11==11.7.91\n - packaging==23.2\n - pandas==2.0.2\n - pillow==10.2.0\n - python-dateutil==2.9.0.post0\n - pytz==2024.1\n - pyyaml==6.0.1\n - regex==2023.12.25\n - requests==2.31.0\n - safetensors==0.4.2\n - scipy==1.10.1\n - six==1.16.0\n - sympy==1.12\n - tokenizers==0.15.2\n - torch==2.0.1\n - torchvision==0.15.2\n - tqdm==4.66.2\n - transformers==4.38.2\n - triton==2.0.0\n - typing-extensions==4.10.0\n - tzdata==2024.1\n - urllib3==2.2.1\n - wcwidth==0.2.13\nprefix: ./miniconda3/envs/bioclip-test\n"
},
{
"path": "scripts/README.md",
"content": "# Imageomics-Specific Scripts\n\n`inat21_to_wds.py` converts raw iNat21 images to a webdataset format and holds 1K classes out for an unseen evaluation. To use it, you will have to edit some variables in the script (`inat_root`, `output_root`). **Note: this script is outdated because we use a combination of scripts in `scripts/evobio10m/`, described in [training-data-osc.md](/docs/imageomics/training-data-osc.md)**\n\n`inat_common_names.py` gets a mapping of scientific names to common names for all species in the iNat21 dataset. In the future, this will be expanded to all species on iNaturalist, not just those in the iNat21 splits. You don't need to run this script because the mapping is already committed to version control.\n\n`evobio10m/check_taxa.py` reads in the full dataset CSV from [Step 6](/docs/imageomics/treeoflife10m.md) and checks for gaps in the taxonomic hierarchy:\n```bash\npython scripts/evobio10m/check_taxa.py \n```\nThe only requirement is `pandas`.\nIt prints warnings indicating the extent of gaps when they occur. A [test CSV](https://huggingface.co/datasets/imageomics/ToL-EDA/blob/main/data/tol_hierarchy_test.csv) for this script can be found on the [ToL-EDA HF repo](https://huggingface.co/datasets/imageomics/ToL-EDA). \n\n`evobio10m/taxa_viz.py` reads in the full dataset CSV from [Step 6](/docs/imageomics/treeoflife10m.md) and generates treemaps from kingdom and phylum down to family rank. Saves treemaps as both `HTML` (interactive) and `PDF` (static to print or display); defaults to `data/visuals` folder if no path given for visuals.\n```bash\npython scripts/evobio10m/taxa_viz.py --output_path \n```\nRequirements given in `requirements-viz.txt`. The test CSV for the `check_taxa` script can be used. The only requirements for the CSV are that it contain the taxa `kingdom` through `family`. If a deeper tree is desired, just add more taxa to the `TAXA` variable at the top of the script; `family` was set as the base due to the size of the dataset.\n\n`evobio10m/get_data_stats.py` reads in the full dataset CSV from [Step 6](/docs/imageomics/treeoflife10m.md) and calculates the total number of unique 7-tuples (kingdom through species) in the full dataset and in each constituent data source (EOL, iNat21, and BIOSCAN-1M). This information is saved to `data/stats` folder by default if no path is given.\n```bash\npython scripts/evobio10m/get_data_stats.py --output_path \n```\nThe only requirement is `pandas`.\nThe test CSV for the `check_taxa` script can be used.\n"
},
{
"path": "scripts/download_data.sh",
"content": "!#/usr/bin/env bash\n\necho \"Getting eol data\"\nmkdir -p data/eol\npushd data/eol\n\necho \"Getting provider_ids.csv\"\nwget https://eol.org/data/provider_ids.csv.gz\ngunzip provider_ids.csv.gz\n\necho \"Getting dh21/taxon.tab\"\nwget https://opendata.eol.org/dataset/0a023d9a-f8c3-4c80-a8d1-1702475cda18/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8/download/dh21.zip\nunzip -d dh21 dh21.zip\nrm dh21.zip\n\necho \"Getting vernacularnames.csv\"\nwget https://opendata.eol.org/dataset/d40073b7-1da7-428a-a404-9233f5153147/resource/bf63dc02-a7ff-416e-b252-e77e23c29d5c/download/vernacularnames.csv\n\necho \"Getting trait_bank\"\nwget https://editors.eol.org/other_files/SDR/traits_all.zip\nunzip traits_all.zip\n\npopd\n\necho \"Getting inaturalist data\"\nmkdir -p data/inat\npushd data/inat\n\necho \"Getting .dwca files\"\nwget https://www.inaturalist.org/taxa/inaturalist-taxonomy.dwca.zip\nunzip -d dwca inaturalist-taxonomy.dwca.zip\nrm inaturalist-taxonomy.dwca.zip\n\npopd"
},
{
"path": "scripts/evobio10m/check_taxa.py",
"content": "import logging\nimport sys\n\nimport pandas as pd\n\n# initialize logger\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\nlogging.basicConfig()\n\n# Output of make_statistics.py should be fed in\nEXPECTED_COLUMNS = [\n \"treeoflife_id\",\n \"eol_content_id\",\n \"eol_page_id\",\n \"bioscan_part\",\n \"bioscan_filename\",\n \"inat21_filename\",\n \"inat21_cls_name\",\n \"inat21_cls_num\",\n \"kingdom\",\n \"phylum\",\n \"class\",\n \"order\",\n \"family\",\n \"genus\",\n \"species\",\n \"common\",\n]\n\nTAXA = [\"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\"]\n\n\ndef check_kingdom(df):\n \"\"\"\n This function checks the number of distinct kingdoms labeled in the dataset (should be 3: Animalia, Plantae, and Fungi).\n Logs a warning with the number included if it's not 3. Warning is printed to terminal, not saved to file.\n\n Parameters:\n -----------\n df - DataFrame with taxonomic hierarchy as columns.\n\n \"\"\"\n # check we have only 3 kingdoms\n num_kingdoms = df.kingdom.nunique()\n if num_kingdoms != 3:\n logging.warning(f\"There are {num_kingdoms} kingdoms instead of 3.\")\n\n\ndef check_hierarchy(df):\n \"\"\"\n Function to check for gaps in the taxonomic hierarchy above the lowest known rank (eg., no missing 'family' if 'genus' specified).\n Logs gaps as warnings. These are printed to terminal, not saved to file.\n\n Parameters:\n -----------\n df - DataFrame with taxonomic hierarchy as columns.\n\n \"\"\"\n # Want to check hierarchy is backfilled from genus up (if only species labeled, requires genus to diambiguate)\n # and similarly for at least family and order since those are lowest ranks for most BIOSCAN\n temp = df.loc[df.genus.notna()]\n for taxon in TAXA[:-2]:\n if temp[taxon].isna().sum() > 0:\n logging.warning(\n f\"{temp[taxon].isna().sum()} entries are missing rank {taxon}, but have genus label.\"\n )\n\n # check family and above, but don't duplicate effort\n missing_genera = df.loc[df.genus.isna()]\n temp_fam = missing_genera.loc[missing_genera.family.notna()]\n for taxon in TAXA[:-3]:\n if temp_fam[taxon].isna().sum() > 0:\n logging.warning(\n f\"{temp_fam[taxon].isna().sum()} entries are missing rank {taxon}, but have family label.\"\n )\n\n # check order and above, but don't duplicate effort\n missing_family = missing_genera.loc[missing_genera.family.isna()]\n temp_order = missing_family.loc[missing_family.order.notna()]\n for taxon in TAXA[:-4]:\n if temp_order[taxon].isna().sum() > 0:\n logging.warning(\n f\"{temp_order[taxon].isna().sum()} entries are missing rank {taxon}, but have order label.\"\n )\n\n # check if higher order taxa are filled in for non-null species missing genus\n temp_species = missing_genera.loc[missing_genera.species.notna()]\n num = temp_species.shape[0]\n for taxon in TAXA[:-2]:\n if temp_species[taxon].isna().sum() < num:\n logging.warning(\n f\"{num - temp_species[taxon].isna().sum()} entries have {taxon} and species labels but no genus.\"\n )\n\n\ndef check_sci_name(df):\n \"\"\"\n This function checks the number of words in the species column for each sample.\n Logs a warning with the number that have more than one word indicating the potential for both genus and species to be recorded.\n Warning is printed to terminal, not saved to file.\n\n Parameters:\n -----------\n df - DataFrame with taxonomic hierarchy as columns.\n\n \"\"\"\n # Check for scientific name in species column (i.e., genus speices in species column, may correspond to missing genus)\n count = 0\n for species in list(df.loc[df[\"species\"].notna(), \"species\"]):\n if len(species.split(\" \")) > 1:\n count += 1\n if count > 0:\n logging.warning(\n f\"There are {count} samples with species label longer than one word.\"\n )\n\n\ndef check_id(df):\n \"\"\"\n This function checks the species column for `(unidentified)`, since this appears in EOL data which is regularly updated.\n Logs a warning with the number of entries where species is labeled `(unidentified)`.\n Warning is printed to terminal, not saved to file.\n\n Parameters:\n -----------\n df - DataFrame with taxonomic hierarchy as columns.\n \"\"\"\n num_noID = len(df.loc[df.species == \"(unidentified)\"])\n if num_noID > 0:\n logging.warning(f\"{num_noID} species are labeled as '(unidentified)'.\")\n\n\ndef check_common(df):\n \"\"\"\n This function checks the common column for nulls, since this column should be filled with either common name or lowest order taxa.\n Logs a warning with the number of entries where common is null.\n Warning is printed to terminal, not saved to file.\n\n Parameters:\n -----------\n df - DataFrame with data source and common as columns.\n \"\"\"\n missing_common = df.loc[df.common.isna()]\n num_no_common = len(missing_common)\n if num_no_common > 0:\n logging.warning(f\"{num_no_common} entries have null common. They are from {missing_common.data_source.unique()}.\")\n\n # print the first and last 5 entries with missing common\n if num_no_common > 0:\n logging.warning(f\"First 5 entries with missing common: {missing_common.head()}\")\n logging.warning(f\"Last 5 entries with missing common: {missing_common.tail()}\")\n\n\n\n\ndef main():\n # check for file\n if len(sys.argv) == 1:\n sys.exit(\"Please provide a source CSV file.\")\n else:\n try:\n df = pd.read_csv(sys.argv[1], low_memory=False)\n except Exception as e:\n sys.exit(e)\n\n # check for columns\n missing_cols = []\n for col in EXPECTED_COLUMNS:\n if col not in list(df.columns):\n missing_cols.append(col)\n if len(missing_cols) > 0:\n sys.exit(f\"Source CSV does not have {missing_cols} columns.\")\n\n # If split column included, remove \"train_small\" entries as they are duplicates from \"train\".\n if \"split\" in list(df.columns):\n df = df.loc[df.split != \"train_small\"]\n\n # Add data_source column to pin-point missing info \n df.loc[df[\"inat21_filename\"].notna(), \"data_source\"] = \"iNat21\"\n df.loc[df[\"bioscan_filename\"].notna(), \"data_source\"] = \"BIOSCAN\"\n df.loc[df[\"eol_content_id\"].notna(), \"data_source\"] = \"EOL\"\n\n # Run checks\n # only pass taxa columns for efficiency (source and common for final check)\n check_kingdom(df[TAXA])\n check_hierarchy(df[TAXA])\n check_sci_name(df[TAXA])\n check_id(df[TAXA])\n check_common(df[[\"data_source\", \"common\"]])\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "scripts/evobio10m/check_wds.py",
"content": "import argparse\nimport sys\nimport tarfile\nimport webdataset as wds\nfrom torch.utils.data import DataLoader\n\ndef log_and_continue(err):\n if isinstance(err, tarfile.ReadError) and len(err.args) == 3:\n print(err.args[2])\n return True\n if isinstance(err, ValueError):\n return True\n raise err\n\ndef eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--shardlist\", required=True, help=\"Path to all shards in braceexpand format.\"\n )\n parser.add_argument(\n \"--workers\", type=int, default=16, help=\"Number of worker processes\"\n )\n parser.add_argument(\"--batch-size\", type=int, default=256, help=\"Batch size\")\n parser.add_argument(\"--log-every\", type=int, default=100, help=\"How often to log\")\n args = parser.parse_args()\n\n keys = (\"__key__\", \"jpg\", \"sci.txt\", \"com.txt\", \"sci_com.txt\", \"taxontag_com.txt\")\n \n dataset = wds.DataPipeline(\n wds.SimpleShardList(args.shardlist),\n wds.tarfile_to_samples(handler=log_and_continue),\n wds.decode(\"torchrgb\"),\n wds.to_tuple(*keys, handler=log_and_continue),\n )\n \n dataloader = DataLoader(\n dataset, num_workers=args.workers, batch_size=args.batch_size\n )\n\n itr = iter(dataloader)\n batches = 0\n total_examples = 0\n while True:\n try:\n batch = next(itr)\n batch_size = len(batch[0])\n batches += 1\n total_examples += batch_size\n\n if batches % args.log_every == 0:\n eprint(f\"{batches} batches / {total_examples} examples (current batch size: {batch_size})\")\n except StopIteration:\n break\n\n eprint(f\"Success! Processed {batches} batches with a total of {total_examples} examples.\")\n"
},
{
"path": "scripts/evobio10m/get_data_stats.py",
"content": "import pandas as pd\nfrom pathlib import Path\nimport argparse\nimport sys\n\n# Output of make_statistics.py (catalog.csv) should be fed in,\n# will also work with predicted-catalog.csv.\nEXPECTED_COLUMNS = [\n \"treeoflife_id\",\n \"eol_content_id\",\n \"eol_page_id\",\n \"bioscan_part\",\n \"bioscan_filename\",\n \"inat21_filename\",\n \"inat21_cls_name\",\n \"inat21_cls_num\",\n \"kingdom\",\n \"phylum\",\n \"class\",\n \"order\",\n \"family\",\n \"genus\",\n \"species\",\n \"common\",\n]\n\nSOURCE_TAXA = [\n \"data_source\",\n \"kingdom\",\n \"phylum\",\n \"class\",\n \"order\",\n \"family\",\n \"genus\",\n \"species\",\n \"common\",\n]\n\nTAXA = [\"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\"]\n\nDATA_SOURCES = [\"TreeOfLife-10M\", \"iNat21\", \"BIOSCAN\", \"EOL\"]\n\n\ndef get_taxa_info(df, stats_path):\n \"\"\"\n Function to generate taxonomic statistics and save to CSV at desired location.\n Calculates total number of unique 7-tuples in full dataset and each constituent data source.\n\n Parameters:\n -----------\n df - DataFrame with data source, taxonomic hierarchy, and common as columns.\n stats_path - String. Path to location in which to save the stats file.\n \"\"\"\n print(\"Fetching taxonomic stats\")\n unique_taxa_counts = []\n # Get number of unique 7-tuples in full dataset and each constituent data source.\n for data_source in DATA_SOURCES:\n df_temp = df.copy()\n if data_source != \"TreeOfLife-10M\":\n df_temp = df_temp.loc[df_temp.data_source == data_source]\n df_temp[\"duplicate\"] = df_temp.duplicated(subset=TAXA, keep=\"first\")\n df_unique_taxa = df_temp.loc[~df_temp[\"duplicate\"]]\n num_unique_taxa = df_unique_taxa.shape[0]\n unique_taxa_counts.append(num_unique_taxa)\n\n # Make DataFrame with these taxa stats\n taxa_counts_df = pd.DataFrame(\n data={\"data_source\": DATA_SOURCES, \"num_unique_taxa\": unique_taxa_counts}\n )\n\n # Save to CSV\n taxa_counts_df.to_csv(stats_path, index=False)\n\n\ndef main(src_csv, dest_dir):\n # Check CSV compatibility\n try:\n print(\"Reading your file\")\n df = pd.read_csv(src_csv, low_memory=False)\n except Exception as e:\n sys.exit(e)\n\n # Check for columns\n missing_cols = []\n for col in EXPECTED_COLUMNS:\n if col not in list(df.columns):\n missing_cols.append(col)\n if len(missing_cols) > 0:\n sys.exit(f\"Source CSV does not have {missing_cols} columns.\")\n\n print(\"Processing your file\")\n # If split column included, remove \"train_small\" entries as they are duplicates from \"train\".\n if \"split\" in list(df.columns):\n df = df.loc[df.split != \"train_small\"]\n\n # Add \"data_source\" column to pin-point missing info.\n df.loc[df[\"inat21_filename\"].notna(), \"data_source\"] = \"iNat21\"\n df.loc[df[\"bioscan_filename\"].notna(), \"data_source\"] = \"BIOSCAN\"\n df.loc[df[\"eol_content_id\"].notna(), \"data_source\"] = \"EOL\"\n\n # Check filepath given by user\n dest_dir_path = Path(dest_dir)\n if not dest_dir_path.is_absolute():\n # Use bioclip as reference folder\n base_path = Path(__file__).parent.parent.parent.resolve()\n stats_path = base_path / dest_dir_path\n else:\n stats_path = dest_dir_path\n\n stats_path.mkdir(parents=True, exist_ok=True)\n\n # Reduce DataFrame for easier processing (\"data_source\", taxa, and \"common\" columns).\n get_taxa_info(df[SOURCE_TAXA], str(stats_path / \"taxa_stats.csv\"))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Generate taxonomic statistics file\")\n parser.add_argument(\"input_file\", help=\"Path to the input CSV file\")\n parser.add_argument(\n \"--output_path\",\n default=\"data/stats\",\n required=False,\n help=\"Path to the folder for output stats file\",\n )\n args = parser.parse_args()\n\n main(args.input_file, args.output_path)\n"
},
{
"path": "scripts/evobio10m/make_catalog.py",
"content": "import argparse\nimport csv\nimport logging\nimport os\nimport tarfile\n\nimport webdataset as wds\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom imageomics import disk, evobio10m, naming\n\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\nlogger = logging.getLogger()\n\nheaders = (\n \"split\",\n \"treeoflife_id\",\n \"eol_content_id\",\n \"eol_page_id\",\n \"bioscan_part\",\n \"bioscan_filename\",\n \"inat21_filename\",\n \"inat21_cls_name\",\n \"inat21_cls_num\",\n \"kingdom\",\n \"phylum\",\n \"class\",\n \"order\",\n \"family\",\n \"genus\",\n \"species\",\n \"common\",\n)\n\n\ndef init_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--dir\",\n required=True,\n help=\"Directory with split directories, each which should contain .tar shard files.\",\n )\n parser.add_argument(\n \"--workers\", type=int, default=16, help=\"Number of worker processes\"\n )\n parser.add_argument(\"--batch-size\", type=int, default=256, help=\"Batch size\")\n parser.add_argument(\"--tag\", required=True, help=\"Tag for this run.\")\n parser.add_argument(\"--db\", required=True, help=\"Path to mapping.sqlite\")\n\n return parser\n\n\ndef log_and_continue(err):\n if isinstance(err, tarfile.ReadError) and len(err.args) == 3:\n logger.warn(err.args[2])\n return True\n\n if isinstance(err, ValueError):\n return True\n\n raise err\n\n\ndef human(num):\n levels = [(\"G\", 1_000_000_000), (\"M\", 1_000_000), (\"K\", 1_000)]\n prefix = \"-\" if num < 0 else \"\"\n num = abs(num)\n for suffix, val in levels:\n if num >= val:\n return f\"{prefix}{num / val:.1f}{suffix}\"\n\n if num < 1:\n return f\"{prefix}{num:.2g}\"\n\n return f\"{prefix}{num}\"\n\n\ndef write_rows(split_dir, writer):\n shardlist = [\n os.path.join(split_dir, shard)\n for shard in os.listdir(split_dir)\n if shard.endswith(\".tar\")\n ]\n\n dataset = wds.DataPipeline(\n wds.SimpleShardList(shardlist),\n # Without this line, you will get num_workers copies\n wds.split_by_worker,\n wds.tarfile_to_samples(handler=log_and_continue),\n wds.decode(\"torchrgb\"),\n wds.to_tuple(*keys, handler=log_and_continue),\n )\n dataloader = DataLoader(\n dataset, num_workers=args.workers, batch_size=args.batch_size\n )\n seen = set()\n for batch in tqdm(dataloader):\n for key, common in zip(*batch):\n assert key not in seen\n seen.add(key)\n\n data_id = key_lookup[key]\n if data_id.eol_page_id:\n taxon, _ = eol_name_lookup[data_id.eol_page_id]\n elif data_id.bioscan_filename:\n taxon, _ = bioscan_name_lookup[key]\n elif data_id.inat21_cls_name:\n taxon, _ = inat21_name_lookup[data_id.inat21_cls_num]\n else:\n raise ValueError(data_id)\n\n writer.writerow(\n (split, key, *data_id.to_tuple(), *taxon.to_tuple(), common)\n )\n\n\nif __name__ == \"__main__\":\n parser = init_parser()\n args = parser.parse_args()\n\n db = evobio10m.get_db(args.db)\n logger.info(\"Started.\")\n\n eol_name_lookup = naming.load_name_lookup(disk.eol_name_lookup_json, keytype=int)\n inat21_name_lookup = naming.load_name_lookup(\n disk.inat21_name_lookup_json, keytype=int\n )\n bioscan_name_lookup = naming.load_name_lookup(disk.bioscan_name_lookup_json)\n logger.info(\"Loaded name lookups.\")\n\n key_lookup = {}\n # EOL\n stmt = \"SELECT content_id, page_id, evobio10m_id FROM eol\"\n for content, page, evobio10m_id in db.execute(stmt).fetchall():\n key_lookup[evobio10m_id] = evobio10m.DatasetId(\n eol_content_id=content, eol_page_id=page\n )\n\n # Bioscan\n stmt = \"SELECT part, filename, evobio10m_id FROM bioscan\"\n for part, filename, evobio10m_id in db.execute(stmt).fetchall():\n key_lookup[evobio10m_id] = evobio10m.DatasetId(\n bioscan_part=part, bioscan_filename=filename\n )\n\n # iNat21\n stmt = \"SELECT filename, cls_name, cls_num, evobio10m_id FROM inat21\"\n for filename, cls_name, cls_num, evobio10m_id in db.execute(stmt).fetchall():\n key_lookup[evobio10m_id] = evobio10m.DatasetId(\n inat21_filename=filename,\n inat21_cls_name=cls_name,\n inat21_cls_num=cls_num,\n )\n\n logger.info(\"Loaded keys.\")\n\n keys = (\"__key__\", \"common_name.txt\")\n\n outfile = os.path.join(args.dir, \"catalog.csv\")\n with open(outfile, \"w\", newline=\"\") as fd:\n writer = csv.writer(fd)\n writer.writerow(headers)\n\n for split in os.listdir(args.dir):\n split_dir = os.path.join(args.dir, split)\n if os.path.isfile(split_dir):\n logger.info(\n \"Skipping file because it's not a directory. [file: %s]\", split_dir\n )\n continue\n\n write_rows(split_dir, writer)\n logger.info(\"Wrote split. [split: %s, file: %s]\", split, outfile)\n"
},
{
"path": "scripts/evobio10m/make_catalog_reproduce.py",
"content": "import argparse\nimport csv\nimport logging\nimport os\nimport tarfile\n\nimport webdataset as wds\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom imageomics import disk_reproduce, evobio10m, naming_reproduce\n\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\nlogger = logging.getLogger()\n\nheaders = (\n \"split\",\n \"treeoflife_id\",\n \"eol_content_id\",\n \"eol_page_id\",\n \"bioscan_part\",\n \"bioscan_filename\",\n \"inat21_filename\",\n \"inat21_cls_name\",\n \"inat21_cls_num\",\n \"kingdom\",\n \"phylum\",\n \"class\",\n \"order\",\n \"family\",\n \"genus\",\n \"species\",\n \"common\",\n)\n\n\ndef init_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--dir\",\n required=True,\n help=\"Directory with split directories, each which should contain .tar shard files.\",\n )\n parser.add_argument(\n \"--workers\", type=int, default=16, help=\"Number of worker processes\"\n )\n parser.add_argument(\"--batch-size\", type=int, default=256, help=\"Batch size\")\n parser.add_argument(\"--tag\", required=True, help=\"Tag for this run.\")\n parser.add_argument(\"--db\", required=True, help=\"Path to mapping.sqlite\")\n\n return parser\n\n\ndef log_and_continue(err):\n if isinstance(err, tarfile.ReadError) and len(err.args) == 3:\n logger.warn(err.args[2])\n return True\n\n if isinstance(err, ValueError):\n return True\n\n raise err\n\n\ndef human(num):\n levels = [(\"G\", 1_000_000_000), (\"M\", 1_000_000), (\"K\", 1_000)]\n prefix = \"-\" if num < 0 else \"\"\n num = abs(num)\n for suffix, val in levels:\n if num >= val:\n return f\"{prefix}{num / val:.1f}{suffix}\"\n\n if num < 1:\n return f\"{prefix}{num:.2g}\"\n\n return f\"{prefix}{num}\"\n\n\ndef write_rows(split_dir, writer, split):\n shardlist = [\n os.path.join(split_dir, shard)\n for shard in os.listdir(split_dir)\n if shard.endswith(\".tar\")\n ]\n\n if not shardlist:\n logger.warn(f\"No .tar files found in directory {split_dir}. Skipping this directory.\")\n return\n\n dataset = wds.DataPipeline(\n wds.SimpleShardList(shardlist),\n # Without this line, you will get num_workers copies\n wds.split_by_worker,\n wds.tarfile_to_samples(handler=log_and_continue),\n wds.decode(\"torchrgb\"),\n wds.to_tuple(*keys, handler=log_and_continue),\n )\n dataloader = DataLoader(\n dataset, num_workers=args.workers, batch_size=args.batch_size\n )\n seen = set()\n for batch in tqdm(dataloader):\n for key, common in zip(*batch):\n assert key not in seen\n seen.add(key)\n\n data_id = key_lookup[key]\n if data_id.eol_page_id:\n taxon_data = eol_name_lookup[data_id.eol_page_id]\n logger.debug(f\"eol_name_lookup[{data_id.eol_page_id}] = {taxon_data}\")\n if len(taxon_data) != 3:\n logger.error(f\"Unexpected value in eol_name_lookup: {taxon_data}\")\n taxon, common_name, _ = taxon_data\n elif data_id.bioscan_filename:\n taxon_data = bioscan_name_lookup[key]\n logger.debug(f\"bioscan_name_lookup[{key}] = {taxon_data}\")\n if len(taxon_data) != 2:\n logger.error(f\"Unexpected value in bioscan_name_lookup: {taxon_data}\")\n taxon, common_name, _ = taxon_data\n elif data_id.inat21_cls_name:\n taxon_data = inat21_name_lookup[data_id.inat21_cls_num]\n logger.debug(f\"inat21_name_lookup[{data_id.inat21_cls_num}] = {taxon_data}\")\n if len(taxon_data) != 2:\n logger.error(f\"Unexpected value in inat21_name_lookup: {taxon_data}\")\n taxon, common_name, _ = taxon_data\n else:\n raise ValueError(data_id)\n\n # Use get_common to generate the common name if not available\n common_name = naming_reproduce.get_common(taxon, common_name)\n\n writer.writerow(\n (split, key, *data_id.to_tuple(), *taxon.to_tuple(), common_name)\n )\n\n\nif __name__ == \"__main__\":\n parser = init_parser()\n args = parser.parse_args()\n\n db = evobio10m.get_db(args.db)\n logger.info(\"Started.\")\n\n eol_name_lookup = naming_reproduce.load_name_lookup(disk_reproduce.eol_name_lookup_json, keytype=int)\n inat21_name_lookup = naming_reproduce.load_name_lookup(\n disk_reproduce.inat21_name_lookup_json, keytype=int\n )\n bioscan_name_lookup = naming_reproduce.load_name_lookup(disk_reproduce.bioscan_name_lookup_json)\n logger.info(\"Loaded name lookups.\")\n\n key_lookup = {}\n # EOL\n stmt = \"SELECT content_id, page_id, evobio10m_id FROM eol\"\n for content, page, evobio10m_id in db.execute(stmt).fetchall():\n key_lookup[evobio10m_id] = evobio10m.DatasetId(\n eol_content_id=content, eol_page_id=page\n )\n\n # Bioscan\n stmt = \"SELECT part, filename, evobio10m_id FROM bioscan\"\n for part, filename, evobio10m_id in db.execute(stmt).fetchall():\n key_lookup[evobio10m_id] = evobio10m.DatasetId(\n bioscan_part=part, bioscan_filename=filename\n )\n\n # iNat21\n stmt = \"SELECT filename, cls_name, cls_num, evobio10m_id FROM inat21\"\n for filename, cls_name, cls_num, evobio10m_id in db.execute(stmt).fetchall():\n key_lookup[evobio10m_id] = evobio10m.DatasetId(\n inat21_filename=filename,\n inat21_cls_name=cls_name,\n inat21_cls_num=cls_num,\n )\n\n logger.info(\"Loaded keys.\")\n\n keys = (\"__key__\", \"common_name.txt\")\n\n outfile = os.path.join(args.dir, \"catalog.csv\")\n with open(outfile, \"w\", newline=\"\") as fd:\n writer = csv.writer(fd)\n writer.writerow(headers)\n\n for split in os.listdir(args.dir):\n split_dir = os.path.join(args.dir, split)\n if os.path.isfile(split_dir):\n logger.info(\n \"Skipping file because it's not a directory. [file: %s]\", split_dir\n )\n continue\n\n write_rows(split_dir, writer, split)\n logger.info(\"Wrote split. [split: %s, file: %s]\", split, outfile)\n"
},
{
"path": "scripts/evobio10m/make_license_files.py",
"content": "\"\"\"\nMake license files for the various datasets that make up TreeOfLife-10M.\n\"\"\"\nimport argparse\nimport csv\nimport logging\nimport pathlib\nimport itertools\n\nfrom tqdm import tqdm\n\nfrom imageomics import disk, evobio10m\n\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\nlogger = logging.getLogger(\"main\")\n\n\ndef init_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--outdir\", required=True, help=\"Path to output directory.\")\n parser.add_argument(\"--db\", required=True, help=\"Path to mapping.sqlite\")\n\n return parser\n\n\ndef manifest_filepath(i):\n return pathlib.Path(disk.eol_media_manifest_dir) / f\"media_manifest_{i}.csv\"\n\n\ndef get_tol10m_ids(eol_content_ids, eol_page_ids):\n assert len(eol_content_ids) == len(eol_page_ids)\n placeholders = \",\".join([\"?\"] * len(eol_content_ids))\n stmt = f\"SELECT evobio10m_id, content_id, page_id FROM eol WHERE content_id IN ({placeholders}) AND page_id IN ({placeholders})\"\n\n results = {}\n for tol10m_id, content_id, page_id in db.execute(\n stmt, eol_content_ids + eol_page_ids\n ).fetchall():\n results[(content_id, page_id)] = tol10m_id\n return results\n\n\ndef batched(iterable, n):\n # batched('ABCDEFG', 3) --> ABC DEF G\n if n < 1:\n raise ValueError(\"n must be at least one\")\n it = iter(iterable)\n while batch := tuple(itertools.islice(it, n)):\n yield batch\n\n\ndef write_from_manifest(reader, writer):\n # 999 is the maximum number of placeholders in the sqlite3 version on OSC.\n # Since we look for both page ids and content ids, we need 999 // 2\n for batch in tqdm(batched(reader, 999 // 2)):\n eol_content_ids, eol_page_ids, *_ = zip(*batch)\n\n results = get_tol10m_ids(eol_content_ids, eol_page_ids)\n\n for eol_content_id, eol_page_id, _, _, license, owner in batch:\n eol_content_id = int(eol_content_id)\n eol_page_id = int(eol_page_id)\n\n tol10m_id = results.get((eol_content_id, eol_page_id))\n if not tol10m_id:\n continue\n writer.writerow([tol10m_id, eol_content_id, eol_page_id, license, owner])\n\n\n\nif __name__ == \"__main__\":\n parser = init_parser()\n args = parser.parse_args()\n\n db = evobio10m.get_db(args.db)\n\n with open(pathlib.Path(args.outdir) / \"eol_licenses.csv\", \"w\") as licenses_fd:\n writer = csv.writer(licenses_fd)\n\n # Write headers.\n writer.writerow(\n [\"treeoflife10m_id\", \"eol_content_id\", \"eol_page_id\", \"license\", \"owner\"]\n )\n\n i = 1\n while manifest_filepath(i).is_file():\n logger.info(\"Reading from %s.\", manifest_filepath(i))\n with open(manifest_filepath(i)) as manifest_fd:\n reader = csv.reader(manifest_fd)\n if i == 1:\n next(reader) # skip header rows\n write_from_manifest(reader, writer)\n\n i += 1\n logger.info(\"Finished with %s.\", manifest_filepath(i))\n licenses_fd.flush()\n"
},
{
"path": "scripts/evobio10m/make_mapping.py",
"content": "\"\"\"\nReads all the files and makes a master mapping to specific files in EvoBio-10M. All files in the 10M have a UUID as the key. This script:\n\n1. Creates the initial mapping (without reading the file contents)\n2. Writes all these details to a sqlite database.\n\"\"\"\nimport argparse\nimport logging\nimport multiprocessing\nimport os\nimport tarfile\nimport uuid\n\nfrom imageomics import disk, eol, evobio10m, helpers\n\ndb_write_frequency = 10_000\n\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\n\n\ndef get_global_id():\n return str(uuid.uuid4())\n\n\ndef get_logger():\n return logging.getLogger(f\"p{os.getpid()}\")\n\n\n######################\n# Encyclopedia of Life\n######################\n\n\ndef read_eol_from_tar(imgset_path):\n \"\"\"\n Reads all filenames from an imgset (.tar.gz file), assigns a uuid, then inserts it into a sqlite database.\n \"\"\"\n eol_insert_stmt = \"\"\"\n INSERT INTO eol\n (content_id, page_id, evobio10m_id)\n VALUES\n (?, ?, ?);\n \"\"\"\n db = evobio10m.get_db(db_path)\n\n insert_values = []\n\n # r|gz indcates reading from a gzipped file, streaming only\n with tarfile.open(imgset_path, \"r|gz\") as tar:\n for i, member in enumerate(tar):\n eol_img = eol.ImageFilename.from_filename(member.name)\n\n global_id = get_global_id()\n\n insert_values.append((eol_img.content_id, eol_img.page_id, global_id))\n\n if i % db_write_frequency == 0:\n helpers.executerobustly(db, eol_insert_stmt, insert_values)\n insert_values = []\n\n # flush any leftover values\n helpers.executerobustly(db, eol_insert_stmt, insert_values)\n db.close()\n\n\n#########\n# BIOSCAN\n#########\n\n\ndef read_bioscan_from_part(part):\n bioscan_insert_stmt = \"\"\"\n INSERT INTO bioscan\n (part, filename, evobio10m_id)\n VALUES\n (?, ?, ?);\n \"\"\"\n # each process get its own db connection.\n db = evobio10m.get_db(db_path)\n\n insert_values = []\n partdir = os.path.join(disk.bioscan_root_dir, f\"part{part}\")\n for i, filename in enumerate(os.listdir(partdir)):\n global_id = get_global_id()\n insert_values.append((part, filename, global_id))\n\n if i % db_write_frequency == 0:\n helpers.executerobustly(db, bioscan_insert_stmt, insert_values)\n insert_values = []\n\n # flush any leftover values\n helpers.executerobustly(db, bioscan_insert_stmt, insert_values)\n db.close()\n\n\n########\n# INAT21\n########\n\n\ndef read_inat21_from_clsdir(clsdir):\n inat21_insert_stmt = \"\"\"\n INSERT INTO inat21\n (filename, cls_name, cls_num, evobio10m_id)\n VALUES\n (?, ?, ?, ?);\n \"\"\"\n # each process get its own db connection.\n db = evobio10m.get_db(db_path)\n\n insert_values = []\n for i, filename in enumerate(\n os.listdir(os.path.join(disk.inat21_root_dir, clsdir))\n ):\n global_id = get_global_id()\n\n index, *taxa = clsdir.split(\"_\")\n index = int(index)\n taxon = \"_\".join(taxa)\n insert_values.append((filename, taxon, index, global_id))\n\n if i % db_write_frequency == 0:\n helpers.executerobustly(db, inat21_insert_stmt, insert_values)\n insert_values = []\n\n # flush any leftover values\n helpers.executerobustly(db, inat21_insert_stmt, insert_values)\n db.close()\n\n\ndef worker(queue):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n for func, args in iter(queue.get, sentinel):\n logger.info(f\"Started {func.__name__}({', '.join(map(str, args))})\")\n func(*args)\n logger.info(f\"Finished {func.__name__}({', '.join(map(str, args))})\")\n\n\nsentinel = \"STOP\"\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--tag\", default=\"dev\", help=\"The suffix for the directory.\")\n parser.add_argument(\n \"--workers\", type=int, default=32, help=\"Number of processes to use.\"\n )\n args = parser.parse_args()\n\n # Set up some global variables that depend on CLI args.\n outdir = evobio10m.get_outdir(args.tag)\n os.makedirs(outdir, exist_ok=True)\n db_path = os.path.join(outdir, \"mapping.sqlite\")\n print(f\"Writing to {db_path}.\")\n\n # 1. Create an initial mapping of all images in a sqlite database.\n task_queue = multiprocessing.Queue()\n\n # Submit all tasks\n # EOL\n for imgset_name in sorted(os.listdir(disk.eol_root_dir)):\n assert imgset_name.endswith(\".tar.gz\")\n imgset_path = os.path.join(disk.eol_root_dir, imgset_name)\n task_queue.put((read_eol_from_tar, (imgset_path,)))\n\n # Bioscan\n # 113 parts in bioscan\n for i in range(1, 114):\n task_queue.put((read_bioscan_from_part, (i,)))\n\n # iNat\n for clsdir in os.listdir(disk.inat21_root_dir):\n task_queue.put((read_inat21_from_clsdir, (clsdir,)))\n\n processes = []\n # Start worker processes\n for i in range(args.workers):\n p = multiprocessing.Process(target=worker, args=(task_queue,))\n processes.append(p)\n p.start()\n\n # Stop worker processes\n for i in range(args.workers):\n task_queue.put(sentinel)\n\n for p in processes:\n p.join()\n"
},
{
"path": "scripts/evobio10m/make_metadata.py",
"content": "\"\"\"\nParses all metadata files into a single format that's easily read by make_wds.py.\n\"\"\"\nimport argparse\nimport csv\nimport json\nimport logging\nimport os\n\nfrom tqdm import tqdm\n\nfrom imageomics import disk, eol, evobio10m, naming\n\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\nlogger = logging.getLogger()\n\n\nclass ClassIndex:\n \"\"\"\n Keeps track of ids for each tier and for the total taxon.\n \"\"\"\n\n def __init__(self):\n self.tiered_lookups = [{} for _ in range(7)]\n\n def get(self, taxon, *, raise_if_missing=False):\n taxon = taxon.to_tuple()\n\n ids = []\n name = \"\"\n for i, value in enumerate(taxon):\n name += value\n if name not in self.tiered_lookups[i]:\n if raise_if_missing:\n raise KeyError(f\"Name {name} not in index[{i}]\")\n else:\n self.tiered_lookups[i][name] = len(self.tiered_lookups[i])\n ids.append(self.tiered_lookups[i][name])\n\n return tuple(ids)\n\n\ndef dump_json(obj, path):\n with open(path, \"w\") as fd:\n json.dump(obj, fd)\n logger.info(\"Dumped json object. [file %s]\", path)\n\n\ndef make_bioscan_lookup():\n bioscan_name_lookup = naming.BioscanNameLookup()\n\n lookup = {}\n stmt = \"SELECT evobio10m_id, filename FROM bioscan;\"\n for global_id, filename in tqdm(db.execute(stmt).fetchall(), desc=\"Bioscan\"):\n taxon = bioscan_name_lookup.taxon(filename)\n taxon = upgrader.upgrade(taxon)\n common = common_name_lookup.get(taxon.scientific, \"\")\n lookup[global_id] = (taxon.to_tuple(), common, class_index.get(taxon))\n\n dump_json(lookup, disk.bioscan_name_lookup_json)\n\n\ndef make_inat21_lookup():\n inat21_name_lookup = naming.iNat21NameLookup()\n\n lookup = {}\n stmt = \"SELECT DISTINCT(cls_num) FROM inat21;\"\n for (cls_num,) in tqdm(db.execute(stmt).fetchall(), desc=\"iNat21\"):\n taxon = inat21_name_lookup.taxon(cls_num)\n common = common_name_lookup.get(taxon.scientific, \"\")\n lookup[cls_num] = (taxon.to_tuple(), common, class_index.get(taxon))\n\n dump_json(lookup, disk.inat21_name_lookup_json)\n\n\ndef make_eol_lookup():\n stmt = \"SELECT distinct(page_id) FROM eol;\"\n page_ids = set(id for (id,) in tqdm(db.execute(stmt).fetchall(), desc=\"page ids\"))\n\n eol_name_lookup = eol.EolNameLookup()\n eol_name_lookup.add_low_quality_names(page_ids)\n\n lookup = {}\n\n for page_id in tqdm(page_ids, desc=\"images\"):\n taxon = eol_name_lookup.taxon(page_id)\n if taxon.empty:\n continue\n taxon = upgrader.upgrade(taxon)\n common = common_name_lookup.get(taxon.scientific, \"\")\n lookup[page_id] = (taxon.to_tuple(), common, class_index.get(taxon))\n\n dump_json(lookup, disk.eol_name_lookup_json)\n\n\ndef make_predicted_catalog_csv(outfile):\n \"\"\"\n Makes a predicted-catalog.csv file that's as close as possible to the other catalog.csv that is produced *after* making the webdataset.\n\n This doesn't ignore images that are in the species blacklist, so predicted-catalog.csv will have more rows than catalog.csv.\n \"\"\"\n # Copied from make_catalog_csv in make_wds.py\n headers = (\n \"split\",\n \"treeoflife_id\",\n \"eol_content_id\",\n \"eol_page_id\",\n \"bioscan_part\",\n \"bioscan_filename\",\n \"inat21_filename\",\n \"inat21_cls_name\",\n \"inat21_cls_num\",\n \"kingdom\",\n \"phylum\",\n \"class\",\n \"order\",\n \"family\",\n \"genus\",\n \"species\",\n \"common\",\n \"class_index\",\n )\n\n splits = evobio10m.load_splits(args.db)\n splits.pop(\"train_small\")\n\n def get_split():\n return next(split for split, ids in splits.items() if global_id in ids)\n\n with open(outfile, \"w\", newline=\"\") as fd:\n writer = csv.writer(fd)\n writer.writerow(headers)\n\n # EOL\n name_lookup = naming.load_name_lookup(disk.eol_name_lookup_json, keytype=int)\n stmt = \"SELECT evobio10m_id, content_id, page_id FROM eol;\"\n for global_id, content_id, page_id in db.execute(stmt).fetchall():\n data_id = evobio10m.DatasetId(\n eol_content_id=content_id, eol_page_id=page_id\n )\n if page_id not in name_lookup:\n continue\n taxon, common, cls_idx = name_lookup[page_id]\n common = naming.get_common(taxon, common)\n\n writer.writerow(\n (\n get_split(),\n global_id,\n *data_id.to_tuple(),\n *taxon.to_tuple(),\n common,\n cls_idx,\n )\n )\n logger.info(\"Wrote EOL rows. [file %s]\", outfile)\n\n # Bioscan\n name_lookup = naming.load_name_lookup(disk.bioscan_name_lookup_json)\n stmt = \"SELECT evobio10m_id, part, filename FROM bioscan\"\n for global_id, part, filename in db.execute(stmt).fetchall():\n data_id = evobio10m.DatasetId(bioscan_part=part, bioscan_filename=filename)\n taxon, common, cls_idx = name_lookup[global_id]\n common = naming.get_common(taxon, common)\n writer.writerow(\n (\n get_split(),\n global_id,\n *data_id.to_tuple(),\n *taxon.to_tuple(),\n common,\n cls_idx,\n )\n )\n logger.info(\"Wrote Bioscan rows. [file %s]\", outfile)\n\n # iNat21\n name_lookup = naming.load_name_lookup(disk.inat21_name_lookup_json, keytype=int)\n stmt = \"SELECT evobio10m_id, filename, cls_name, cls_num FROM inat21\"\n for global_id, filename, cls_name, cls_num in db.execute(stmt).fetchall():\n data_id = evobio10m.DatasetId(\n inat21_filename=filename,\n inat21_cls_name=cls_name,\n inat21_cls_num=cls_num,\n )\n taxon, common, cls_idx = name_lookup[cls_num]\n common = naming.get_common(taxon, common)\n writer.writerow(\n (\n get_split(),\n global_id,\n *data_id.to_tuple(),\n *taxon.to_tuple(),\n common,\n cls_idx,\n )\n )\n logger.info(\"Wrote iNat21 rows. [file %s]\", outfile)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--db\", required=True, help=\"Path to mapping.sqlite\")\n args = parser.parse_args()\n\n assert os.path.isfile(args.db)\n db = evobio10m.get_db(args.db)\n logger.info(\"Connected to db. [db: %s]\", args.db)\n\n common_name_lookup = naming.CommonNameLookup()\n dump_json(common_name_lookup, disk.common_name_lookup_json)\n\n upgrader = naming.NameUpgrader()\n\n class_index = ClassIndex()\n\n make_bioscan_lookup()\n make_inat21_lookup()\n make_eol_lookup()\n\n make_predicted_catalog_csv(\n os.path.join(os.path.dirname(args.db), \"predicted-catalog.csv\")\n )\n"
},
{
"path": "scripts/evobio10m/make_splits.py",
"content": "\"\"\"\nDecides which images are part of the validation split\n\"\"\"\n\nimport argparse\nimport os.path\nimport random\n\nfrom tqdm import tqdm\n\nfrom imageomics import evobio10m\n\ninsert_stmt = (\n \"INSERT INTO split (evobio10m_id, is_val, is_train_small) VALUES (?, ?, ?)\"\n)\n\n\ndef make_split(table_name):\n select_stmt = f\"SELECT evobio10m_id FROM {table_name} ORDER BY evobio10m_id\"\n all_ids = set([row[0] for row in db.execute(select_stmt).fetchall()])\n\n random.seed(args.seed)\n val_ids = set(random.sample(list(all_ids), k=len(all_ids) * args.val_split // 100))\n\n train_ids = all_ids - val_ids\n\n train_small_ids = set(\n random.sample(list(train_ids), k=len(train_ids) * args.train_small_split // 100)\n )\n\n for id in tqdm(all_ids, desc=table_name):\n db.execute(insert_stmt, (id, id in val_ids, id in train_small_ids))\n db.commit()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--db\", required=True, help=\"Path to mapping.sqlite\")\n parser.add_argument(\"--seed\", type=int, default=17, help=\"Random seed\")\n parser.add_argument(\n \"--val-split\",\n type=int,\n default=5,\n help=\"Percentage of all data to use for validation\",\n )\n parser.add_argument(\n \"--train-small-split\",\n type=int,\n default=5,\n help=\"Percentage of training data to use for ablation\",\n )\n args = parser.parse_args()\n\n assert os.path.isfile(args.db)\n db = evobio10m.get_db(args.db)\n\n make_split(\"eol\")\n make_split(\"inat21\")\n make_split(\"bioscan\")\n"
},
{
"path": "scripts/evobio10m/make_wds.py",
"content": "\"\"\"\nWrites the training and validation data to webdataset format.\n\"\"\"\nimport argparse\nimport collections\nimport json\nimport logging\nimport multiprocessing\nimport os\nimport tarfile\n\nfrom PIL import Image, ImageFile\n\nfrom imageomics import disk, eol, evobio10m, naming, wds\n\n########\n# CONFIG\n########\n\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\nrootlogger = logging.getLogger(\"root\")\n\nImage.MAX_IMAGE_PIXELS = 30_000**2 # 30_000 pixels per side\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\n\n########\n# SHARED\n########\n\n\ndef load_img(file):\n img = Image.open(file)\n if img.mode in (\"RGBA\", \"P\"):\n img = img.convert(\"RGB\")\n return img.resize(resize_size, resample=Image.BICUBIC)\n\n\ndef load_blacklists():\n image_blacklist = set()\n species_blacklist = set()\n\n with open(disk.seen_in_training_json) as fd:\n for scientific, images in json.load(fd).items():\n image_blacklist |= set(os.path.basename(img) for img in images)\n\n with open(disk.unseen_in_training_json) as fd:\n for scientific, images in json.load(fd).items():\n image_blacklist |= set(os.path.basename(img) for img in images)\n species_blacklist.add(scientific)\n\n return image_blacklist, species_blacklist\n\n\n######################\n# Encyclopedia of Life\n######################\n\n\ndef copy_eol_from_tar(sink, imgset_path):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n\n db = evobio10m.get_db(db_path)\n select_stmt = \"SELECT evobio10m_id, content_id, page_id FROM eol;\"\n evobio10m_id_lookup = {\n (content_id, page_id): evobio10m_id\n for evobio10m_id, content_id, page_id in db.execute(select_stmt).fetchall()\n }\n db.close()\n\n name_lookup = naming.load_name_lookup(disk.eol_name_lookup_json, keytype=int)\n\n # r|gz indcates reading from a gzipped file, streaming only\n with tarfile.open(imgset_path, \"r|gz\") as tar:\n for i, member in enumerate(tar):\n eol_img = eol.ImageFilename.from_filename(member.name)\n if eol_img.raw in image_blacklist:\n continue\n\n if (eol_img.content_id, eol_img.page_id) not in evobio10m_id_lookup:\n logger.warning(\n \"EvoBio10m ID missing. [content: %d, page: %d]\",\n eol_img.content_id,\n eol_img.page_id,\n )\n continue\n\n global_id = evobio10m_id_lookup[(eol_img.content_id, eol_img.page_id)]\n if global_id not in splits[args.split] or global_id in finished_ids:\n continue\n\n if eol_img.page_id not in name_lookup:\n continue\n\n taxon, common, classes = name_lookup[eol_img.page_id]\n\n if taxon.scientific in species_blacklist:\n continue\n\n file = tar.extractfile(member)\n try:\n img = load_img(file).resize(resize_size)\n except OSError as err:\n logger.warning(\n \"Error opening file. Skipping. [tar: %s, err: %s]\", imgset_path, err\n )\n continue\n\n txt_dct = make_txt(taxon, common)\n sink.write(\n {\"__key__\": global_id, \"jpg\": img, **txt_dct, \"classes\": classes}\n )\n\n\n########\n# INAT21\n########\n\n\ndef copy_inat21_from_clsdir(sink, clsdir):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n\n db = evobio10m.get_db(db_path)\n select_stmt = \"SELECT evobio10m_id, filename, cls_num FROM inat21;\"\n evobio10m_id_lookup = {\n (filename, cls_num): evobio10m_id\n for evobio10m_id, filename, cls_num in db.execute(select_stmt).fetchall()\n }\n db.close()\n\n name_lookup = naming.load_name_lookup(disk.inat21_name_lookup_json, keytype=int)\n\n clsdir_path = os.path.join(disk.inat21_root_dir, clsdir)\n for i, filename in enumerate(os.listdir(clsdir_path)):\n filepath = os.path.join(clsdir_path, filename)\n\n cls_num, *_ = clsdir.split(\"_\")\n cls_num = int(cls_num)\n\n if (filename, cls_num) not in evobio10m_id_lookup:\n logger.warning(\n \"Evobio10m ID missing. [image: %s, cls: %d]\", filename, cls_num\n )\n continue\n\n global_id = evobio10m_id_lookup[(filename, cls_num)]\n if global_id not in splits[args.split] or global_id in finished_ids:\n continue\n\n taxon, common, classes = name_lookup[cls_num]\n\n if taxon.scientific in species_blacklist:\n continue\n\n txt_dct = make_txt(taxon, common)\n img = load_img(filepath).resize(resize_size)\n sink.write({\"__key__\": global_id, \"jpg\": img, **txt_dct, \"classes\": classes})\n\n\n#########\n# BIOSCAN\n#########\n\n\ndef copy_bioscan_from_part(sink, part):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n\n db = evobio10m.get_db(db_path)\n select_stmt = \"SELECT evobio10m_id, part, filename FROM bioscan;\"\n evobio10m_id_lookup = {\n (part, filename): evobio10m_id\n for evobio10m_id, part, filename in db.execute(select_stmt).fetchall()\n }\n db.close()\n\n name_lookup = naming.load_name_lookup(disk.bioscan_name_lookup_json)\n\n partdir = os.path.join(disk.bioscan_root_dir, f\"part{part}\")\n for i, filename in enumerate(os.listdir(partdir)):\n if (part, filename) not in evobio10m_id_lookup:\n logger.warning(\n \"EvoBio10m ID missing. [part: %d, filename: %s]\", part, filename\n )\n continue\n\n global_id = evobio10m_id_lookup[(part, filename)]\n if global_id not in splits[args.split] or global_id in finished_ids:\n continue\n\n taxon, common, classes = name_lookup[global_id]\n\n if taxon.scientific in species_blacklist:\n continue\n\n txt_dct = make_txt(taxon, common)\n filepath = os.path.join(partdir, filename)\n img = load_img(filepath).resize(resize_size)\n sink.write({\"__key__\": global_id, \"jpg\": img, **txt_dct, \"classes\": classes})\n\n\n######\n# MAIN\n######\n\n\ndef check_status():\n finished_ids, bad_shards = set(), set()\n for root, dirs, files in os.walk(outdir):\n for file in files:\n if not file.endswith(\".tar\"):\n continue\n\n written = collections.defaultdict(set)\n with tarfile.open(os.path.join(root, file), \"r|\") as tar:\n try:\n for member in tar:\n global_id, *rest = member.name.split(\".\")\n rest = \".\".join(rest)\n written[global_id].add(rest)\n except tarfile.TarError as err:\n print(err)\n continue\n\n # If you change make_txt, update these expected_exts\n expected_exts = {\n \"scientific_name.txt\",\n \"taxonomic_name.txt\",\n \"common_name.txt\",\n \"sci.txt\",\n \"com.txt\",\n \"taxon.txt\",\n \"taxonTag.txt\",\n \"sci_com.txt\",\n \"taxon_com.txt\",\n \"taxonTag_com.txt\",\n \"jpg\",\n }\n for global_id, exts in written.items():\n if exts != expected_exts:\n # Delete all the files with this global_id. But this is impossible\n # with the .tar format. So instead, we delete this entire shard.\n bad_shards.add(os.path.join(root, file))\n break\n else:\n # If we didn't early break, then this file is clean.\n finished_ids.update(set(written.keys()))\n\n return finished_ids, bad_shards\n\n\ndef make_txt(taxon, common):\n assert taxon is not None\n assert not taxon.empty, f\"{common} has no taxon!\"\n\n if not common:\n common = taxon.scientific\n if not common:\n common = taxon.taxonomic\n\n tagged = \" \".join(f\"{tier} {value}\" for tier, value in taxon.tagged)\n\n # IF YOU UPDATE THE KEYS HERE, BE SURE TO UPDATE check_status() TO LOOK FOR ALL OF\n # THESE KEYS. IF YOU DO NOT, THEN check_status() MIGHT ASSUME AN EXAMPLE IS WRITTEN\n # EVEN IF NOT ALL KEYS ARE PRESENT.\n return {\n # Names\n \"scientific_name.txt\": taxon.scientific,\n \"taxonomic_name.txt\": taxon.taxonomic,\n \"common_name.txt\": common,\n # \"A photo of\"... captions\n \"sci.txt\": f\"a photo of {taxon.scientific}.\",\n \"com.txt\": f\"a photo of {common}.\",\n \"taxon.txt\": f\"a photo of {taxon.taxonomic}.\",\n \"taxonTag.txt\": f\"a photo of {tagged}.\",\n \"sci_com.txt\": f\"a photo of {taxon.scientific} with common name {common}.\",\n \"taxon_com.txt\": f\"a photo of {taxon.taxonomic} with common name {common}.\",\n \"taxonTag_com.txt\": f\"a photo of {tagged} with common name {common}.\",\n }\n\n\nsentinel = \"STOP\"\n\n\ndef worker(input):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n with wds.ShardWriter(outdir, shard_counter, digits=6, maxsize=3e9) as sink:\n for func, args in iter(input.get, sentinel):\n logger.info(f\"Started {func.__name__}({', '.join(map(str, args))})\")\n func(sink, *args)\n logger.info(f\"Finished {func.__name__}({', '.join(map(str, args))})\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--width\", type=int, default=224, help=\"Width of resized images.\"\n )\n parser.add_argument(\n \"--height\", type=int, default=224, help=\"Height of resized images.\"\n )\n parser.add_argument(\n \"--split\", choices=[\"train\", \"val\", \"train_small\"], default=\"val\"\n )\n parser.add_argument(\"--tag\", default=\"dev\", help=\"The suffix for the directory.\")\n parser.add_argument(\n \"--workers\", type=int, default=32, help=\"Number of processes to use.\"\n )\n args = parser.parse_args()\n\n # Set up some global variables that depend on CLI args.\n resize_size = (args.width, args.height)\n outdir = f\"{evobio10m.get_outdir(args.tag)}/{args.width}x{args.height}/{args.split}\"\n os.makedirs(outdir, exist_ok=True)\n print(f\"Writing images to {outdir}.\")\n\n db_path = f\"{evobio10m.get_outdir(args.tag)}/mapping.sqlite\"\n\n # Load train/val/train_small splits\n splits = evobio10m.load_splits(db_path)\n\n # Load images already written to tar files to avoid duplicate work.\n # Delete any unfinished shards.\n finished_ids, bad_shards = check_status()\n rootlogger.info(\"Found %d finished examples.\", len(finished_ids))\n rootlogger.warning(\"Found %d bad shards.\", len(bad_shards))\n if bad_shards:\n for shard in bad_shards:\n os.remove(shard)\n rootlogger.warning(\"Deleted shard %d\", shard)\n\n # Load image and species blacklists for rare species\n image_blacklist, species_blacklist = load_blacklists()\n\n # Creates a shared integer\n shard_counter = multiprocessing.Value(\"I\", 0, lock=True)\n\n # All jobs read from this queue\n task_queue = multiprocessing.Queue()\n\n # Submit all tasks\n # EOL\n for imgset_name in sorted(os.listdir(disk.eol_root_dir)):\n assert imgset_name.endswith(\".tar.gz\")\n imgset_path = os.path.join(disk.eol_root_dir, imgset_name)\n task_queue.put((copy_eol_from_tar, (imgset_path,)))\n\n # Bioscan\n # 113 parts in bioscan\n for i in range(1, 114):\n task_queue.put((copy_bioscan_from_part, (i,)))\n\n # iNat\n for clsdir in os.listdir(disk.inat21_root_dir):\n task_queue.put((copy_inat21_from_clsdir, (clsdir,)))\n\n processes = []\n # Start worker processes\n for i in range(args.workers):\n p = multiprocessing.Process(target=worker, args=(task_queue,))\n processes.append(p)\n p.start()\n\n # Stop worker processes\n for i in range(args.workers):\n task_queue.put(sentinel)\n\n for p in processes:\n p.join()\n"
},
{
"path": "scripts/evobio10m/make_wds_reproduce.py",
"content": "\"\"\"\nWrites the training and validation data to webdataset format.\n\"\"\"\nimport argparse\nimport collections\nimport json\nimport logging\nimport multiprocessing\nimport os\nimport tarfile\n\nfrom PIL import Image, ImageFile\n\nfrom imageomics import disk_reproduce, eol_reproduce, evobio10m_reproduce, naming_reproduce, wds\n\n########\n# CONFIG\n########\n\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\nrootlogger = logging.getLogger(\"root\")\n\nImage.MAX_IMAGE_PIXELS = 30_000**2 # 30_000 pixels per side\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\n\n########\n# SHARED\n########\n\n\ndef load_img(file):\n img = Image.open(file)\n if img.mode in (\"RGBA\", \"P\"):\n img = img.convert(\"RGB\")\n return img.resize(resize_size, resample=Image.BICUBIC)\n\n\ndef load_blacklists():\n image_blacklist = set()\n species_blacklist = set()\n\n with open(disk_reproduce.seen_in_training_json) as fd:\n for scientific, images in json.load(fd).items():\n image_blacklist |= set(os.path.basename(img) for img in images)\n\n with open(disk_reproduce.unseen_in_training_json) as fd:\n for scientific, images in json.load(fd).items():\n image_blacklist |= set(os.path.basename(img) for img in images)\n species_blacklist.add(scientific)\n\n return image_blacklist, species_blacklist\n\n\n######################\n# Encyclopedia of Life\n######################\n\n\ndef copy_eol_from_tar(sink, imgset_path):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n\n db = evobio10m_reproduce.get_db(db_path)\n select_stmt = \"SELECT evobio10m_id, content_id, page_id FROM eol;\"\n eol_ids_lookup = {\n evobio10m_id: (content_id, page_id)\n for evobio10m_id, content_id, page_id in db.execute(select_stmt).fetchall()\n }\n db.close()\n\n name_lookup = naming_reproduce.load_name_lookup(disk_reproduce.eol_name_lookup_json, keytype=int)\n\n # r|gz indcates reading from a gzipped file, streaming only\n with tarfile.open(imgset_path, \"r|gz\") as tar:\n for i, member in enumerate(tar):\n eol_img = eol_reproduce.ImageFilename.from_filename(member.name)\n if eol_img.raw in image_blacklist:\n continue\n \n # Match on treeoflife_id filename\n if eol_img.tol_id not in eol_ids_lookup:\n print(f\"Can't find the tol_id {eol_img.tol_id}\")\n logger.warning(\n \"EvoBio10m ID missing. [tol_id: %s]\",\n eol_img.tol_id,\n )\n continue\n\n # fetching page ID\n content_id, page_id = eol_ids_lookup[eol_img.tol_id]\n global_id = eol_img.tol_id\n \n # checking for global id in split\n if global_id not in splits[args.split] or global_id in finished_ids:\n continue\n \n # checking for page id\n if page_id not in name_lookup:\n continue\n \n # using name lookup for taxon, common, classes\n taxon, common, classes = name_lookup[page_id]\n\n if taxon.scientific in species_blacklist:\n continue\n\n file = tar.extractfile(member)\n try:\n img = load_img(file).resize(resize_size)\n except OSError as err:\n logger.warning(\n \"Error opening file. Skipping. [tar: %s, err: %s]\", imgset_path, err\n )\n continue\n\n txt_dct = make_txt(taxon, common) \n # writing EOL\n sink.write(\n {\"__key__\": global_id, \"jpg\": img, **txt_dct} #, \"classes\": classes} # REMOVED \"classes\", unused list of numbers, throws an error as an unknown extension with webdataset\n )\n\n\n########\n# INAT21\n########\n\n\ndef copy_inat21_from_clsdir(sink, clsdir):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n\n db = evobio10m_reproduce.get_db(db_path)\n select_stmt = \"SELECT evobio10m_id, filename, cls_num FROM inat21;\"\n evobio10m_id_lookup = {\n (filename, cls_num): evobio10m_id\n for evobio10m_id, filename, cls_num in db.execute(select_stmt).fetchall()\n }\n db.close()\n\n name_lookup = naming_reproduce.load_name_lookup(disk_reproduce.inat21_name_lookup_json, keytype=int)\n\n clsdir_path = os.path.join(disk_reproduce.inat21_root_dir, clsdir)\n for i, filename in enumerate(os.listdir(clsdir_path)):\n filepath = os.path.join(clsdir_path, filename)\n\n cls_num, *_ = clsdir.split(\"_\")\n cls_num = int(cls_num)\n\n if (filename, cls_num) not in evobio10m_id_lookup:\n logger.warning(\n \"Evobio10m ID missing. [image: %s, cls: %d]\", filename, cls_num\n )\n continue\n\n global_id = evobio10m_id_lookup[(filename, cls_num)]\n if global_id not in splits[args.split] or global_id in finished_ids:\n continue\n\n taxon, common, classes = name_lookup[cls_num]\n\n if taxon.scientific in species_blacklist:\n continue\n\n txt_dct = make_txt(taxon, common) #, classes)\n img = load_img(filepath).resize(resize_size)\n # writing iNat\n sink.write({\"__key__\": global_id, \"jpg\": img, **txt_dct} #, \"classes\": classes} # REMOVED \"classes\", unused list of numbers, throws an error as an unknown extension with webdataset\n )\n\n\n#########\n# BIOSCAN\n#########\n\n\ndef copy_bioscan_from_part(sink, part):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n\n db = evobio10m_reproduce.get_db(db_path)\n select_stmt = \"SELECT evobio10m_id, part, filename FROM bioscan;\"\n evobio10m_id_lookup = {\n (part, filename): evobio10m_id\n for evobio10m_id, part, filename in db.execute(select_stmt).fetchall()\n }\n db.close()\n\n name_lookup = naming_reproduce.load_name_lookup(disk_reproduce.bioscan_name_lookup_json)\n\n partdir = os.path.join(disk_reproduce.bioscan_root_dir, f\"part{part}\")\n for i, filename in enumerate(os.listdir(partdir)):\n if (part, filename) not in evobio10m_id_lookup:\n logger.warning(\n \"EvoBio10m ID missing. [part: %d, filename: %s]\", part, filename\n )\n continue\n\n global_id = evobio10m_id_lookup[(part, filename)]\n if global_id not in splits[args.split] or global_id in finished_ids:\n continue\n\n taxon, common, classes = name_lookup[global_id]\n\n if taxon.scientific in species_blacklist:\n continue\n\n txt_dct = make_txt(taxon, common) #, classes)\n filepath = os.path.join(partdir, filename)\n img = load_img(filepath).resize(resize_size)\n # writing BIOSCAN\n sink.write({\"__key__\": global_id, \"jpg\": img, **txt_dct} #, \"classes\": classes} # REMOVED \"classes\", unused list of numbers, throws an error as an unknown extension with webdataset\n )\n\n\n######\n# MAIN\n######\n\n\ndef check_status():\n finished_ids, bad_shards = set(), set()\n for root, dirs, files in os.walk(outdir):\n for file in files:\n if not file.endswith(\".tar\"):\n continue\n\n written = collections.defaultdict(set)\n with tarfile.open(os.path.join(root, file), \"r|\") as tar:\n try:\n for member in tar:\n global_id, *rest = member.name.split(\".\")\n rest = \".\".join(rest)\n written[global_id].add(rest)\n except tarfile.TarError as err:\n print(err)\n continue\n\n # If you change make_txt, update these expected_texts\n expected_exts = {\n \"scientific_name.txt\",\n \"taxonomic_name.txt\",\n \"common_name.txt\",\n \"sci.txt\",\n \"com.txt\",\n \"taxon.txt\",\n \"taxonTag.txt\",\n \"sci_com.txt\",\n \"taxon_com.txt\",\n \"taxonTag_com.txt\",\n \"jpg\",\n }\n for global_id, exts in written.items():\n if exts != expected_exts:\n # Delete all the files with this global_id. But this is impossible\n # with the .tar format. So instead, we delete this entire shard.\n bad_shards.add(os.path.join(root, file))\n break\n else:\n # If we didn't early break, then this file is clean.\n finished_ids.update(set(written.keys()))\n\n return finished_ids, bad_shards\n\n\ndef make_txt(taxon, common):\n assert taxon is not None\n assert not taxon.empty, f\"{common} has no taxon!\"\n\n # test replacing the two if statements with\n common = naming_reproduce.get_common(taxon, common)\n '''if not common:\n common = taxon.scientific\n if not common:\n common = taxon.taxonomic'''\n\n # ex: kingdom Animalia phylum Arthropoda class ...\n tagged = \" \".join(f\"{tier} {value}\" for tier, value in taxon.tagged)\n\n # IF YOU UPDATE THE KEYS HERE, BE SURE TO UPDATE check_status() TO LOOK FOR ALL OF\n # THESE KEYS. IF YOU DO NOT, THEN check_status() MIGHT ASSUME AN EXAMPLE IS WRITTEN\n # EVEN IF NOT ALL KEYS ARE PRESENT.\n return {\n # Names\n \"scientific_name.txt\": taxon.scientific,\n \"taxonomic_name.txt\": taxon.taxonomic,\n \"common_name.txt\": common,\n # \"A photo of\"... captions\n \"sci.txt\": f\"a photo of {taxon.scientific}.\",\n \"com.txt\": f\"a photo of {common}.\",\n \"taxon.txt\": f\"a photo of {taxon.taxonomic}.\",\n \"taxonTag.txt\": f\"a photo of {tagged}.\",\n \"sci_com.txt\": f\"a photo of {taxon.scientific} with common name {common}.\",\n \"taxon_com.txt\": f\"a photo of {taxon.taxonomic} with common name {common}.\",\n \"taxonTag_com.txt\": f\"a photo of {tagged} with common name {common}.\",\n }\n\n\nsentinel = \"STOP\"\n\n\ndef worker(input):\n logger = logging.getLogger(f\"p{os.getpid()}\")\n with wds.ShardWriter(outdir, shard_counter, digits=6, maxsize=3e9) as sink:\n for func, args in iter(input.get, sentinel):\n logger.info(f\"Started {func.__name__}({', '.join(map(str, args))})\")\n func(sink, *args)\n logger.info(f\"Finished {func.__name__}({', '.join(map(str, args))})\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--width\", type=int, default=224, help=\"Width of resized images.\"\n )\n parser.add_argument(\n \"--height\", type=int, default=224, help=\"Height of resized images.\"\n )\n parser.add_argument(\n \"--split\", choices=[\"train\", \"val\", \"train_small\"], default=\"val\"\n )\n parser.add_argument(\"--tag\", default=\"dev\", help=\"The suffix for the directory.\")\n parser.add_argument(\n \"--workers\", type=int, default=32, help=\"Number of processes to use.\"\n )\n args = parser.parse_args()\n\n # Set up some global variables that depend on CLI args.\n resize_size = (args.width, args.height)\n outdir = f\"{evobio10m_reproduce.get_outdir(args.tag)}/{args.width}x{args.height}/{args.split}\"\n os.makedirs(outdir, exist_ok=True)\n print(f\"Writing images to {outdir}.\")\n\n # db_path = f\"{evobio10m_reproduce.get_outdir(args.tag)}/mapping.sqlite\"\n db_path = os.path.abspath(disk_reproduce.db)\n\n # Load train/val/train_small splits\n splits = evobio10m_reproduce.load_splits(db_path)\n\n # Load images already written to tar files to avoid duplicate work.\n # Delete any unfinished shards.\n finished_ids, bad_shards = check_status()\n rootlogger.info(\"Found %d finished examples.\", len(finished_ids))\n rootlogger.warning(\"Found %d bad shards.\", len(bad_shards))\n if bad_shards:\n for shard in bad_shards:\n os.remove(shard)\n rootlogger.warning(\"Deleted shard %d\", shard)\n\n # Load image and species blacklists for rare species\n image_blacklist, species_blacklist = load_blacklists()\n\n # Creates a shared integer\n shard_counter = multiprocessing.Value(\"I\", 0, lock=True)\n\n # All jobs read from this queue\n task_queue = multiprocessing.Queue()\n\n # Submit all tasks\n # EOL\n for imgset_name in sorted(os.listdir(disk_reproduce.eol_root_dir)):\n assert imgset_name.endswith(\".tar.gz\")\n imgset_path = os.path.join(disk_reproduce.eol_root_dir, imgset_name)\n task_queue.put((copy_eol_from_tar, (imgset_path,)))\n\n # Bioscan\n # 113 parts in bioscan\n for i in range(1, 114):\n task_queue.put((copy_bioscan_from_part, (i,)))\n\n # iNat\n for clsdir in os.listdir(disk_reproduce.inat21_root_dir):\n task_queue.put((copy_inat21_from_clsdir, (clsdir,)))\n\n processes = []\n # Start worker processes\n for i in range(args.workers):\n p = multiprocessing.Process(target=worker, args=(task_queue,))\n processes.append(p)\n p.start()\n\n # Stop worker processes\n for i in range(args.workers):\n task_queue.put(sentinel)\n\n for p in processes:\n p.join()\n"
},
{
"path": "scripts/evobio10m/taxa_viz.py",
"content": "import pandas as pd\nimport plotly.express as px\nfrom pathlib import Path\nimport argparse\nimport sys\n\n# Output of make_statistics.py (catalog.csv) should be fed in,\n# will also work with predicted-catalog.csv or a subset with just the taxa.\n# TAXA are required columns (hierarchy to include in tree).\nTAXA = [\"kingdom\", \"phylum\", \"class\", \"order\", \"family\"]\n\n\ndef get_colors(df, top_level):\n \"\"\"\n Function to define color sequence to use for treemap.\n\n Parameters:\n -----------\n df - DataFrame with taxonomic hierarchy as columns.\n top_level - String. Level at which to start the tree (\"kingdom\" or \"phylum\").\n\n Returns:\n --------\n color_map - Dictionary. Mapping of kingdoms/phyla to color from Plotly Bold color scheme.\n \"\"\"\n # Get list of all possible taxa for the level\n top_level_options = list(df[top_level].unique())\n # Generate color mapping for all kingdoms/phyla\n colors = px.colors.qualitative.Bold\n color_map = {}\n i = 0\n for option in top_level_options:\n # There are only 10 colors in the sequence, so we'll need to loop through it to assign all kingdoms/phyla\n i = i % 10\n color_map[option] = colors[i]\n i += 1\n return color_map\n\n\ndef make_tree(df, color_map, top_level, path_idx):\n \"\"\"\n Function to generate a treemap from the designated top_level rank down to \"family\".\n\n Parameters:\n -----------\n df - DataFrame with taxonomic hierarchy as columns.\n color_map - Dictionary. Mapping of kingdoms/phyla to color from Plotly Bold color scheme.\n top_level - String. Level at which to start the tree (\"kingdom\" or \"phylum\").\n path_idx - Int. Index at which to start for TAXA (0 or 1).\n\n Returns:\n --------\n fig - Treemap figure from the designated top_level rank down to \"family\".\n \"\"\"\n df_rank = df.copy()\n # Distribution of kingdom or phyla and lower taxa (to family) in TreeOfLife10M\n fig = px.treemap(\n df_rank, path=TAXA[path_idx:], color=top_level, color_discrete_map=color_map\n )\n\n # Minimize margins, set aspect ratio to 2:1\n fig.update_scenes(aspectratio={\"x\": 2, \"y\": 1})\n fig.update_layout(font={\"size\": 18}, margin={\"l\": 0, \"r\": 0, \"t\": 0, \"b\": 0})\n return fig\n\n\ndef get_tree(df, top_level):\n \"\"\"\n Function to filter DataFrame for designated top_level taxa, then get associated color mapping and treemap figure.\n\n Parameters:\n -----------\n df - DataFrame with taxonomic hierarchy as columns.\n top_level - String. Level at which to start the tree (\"kingdom\" or \"phylum\").\n\n Returns:\n --------\n fig - Treemap figure from the designated top_level rank down to \"family\".\n \"\"\"\n print(f\"Making {top_level} treemap\")\n df_taxa = df.copy()\n if top_level == \"phylum\":\n # Drop null phylum values\n df_taxa = df_taxa.loc[df_taxa.phylum.notna()]\n # Set index for path\n path_idx = 1\n else:\n # Drop null kingdom values\n df_taxa = df_taxa.loc[df_taxa.kingdom.notna()]\n # Set index for path\n path_idx = 0\n # Fill null lower ranks with \"unknown\" for graphing purposes\n df_taxa = df_taxa.fillna(\"unknown\")\n\n # Get color mapping for chosen top taxon and generate the treemap.\n color_map = get_colors(df_taxa, top_level)\n fig = make_tree(df_taxa, color_map, top_level, path_idx)\n return fig\n\n\ndef save_trees(df, viz_path):\n \"\"\"\n Function to get and save treemap figures starting at both kingdom and phylum level.\n Saves as interactive HTML files and PDFs in the designated location.\n\n Parameters:\n -----------\n df - DataFrame with taxonomic hierarchy as columns.\n viz_path - Path object. Path to location in which to save the figures.\n\n \"\"\"\n # Make phylum tree\n fig_phyla = get_tree(df, \"phylum\")\n # Make kingdom tree\n fig_kingdom = get_tree(df, \"kingdom\")\n\n # Save HTML of trees for interactive use\n phyla_html_path = str(viz_path / \"phyla_ToL_tree.html\")\n king_html_path = str(viz_path / \"kingdom_ToL_tree.html\")\n fig_phyla.write_html(phyla_html_path)\n fig_kingdom.write_html(king_html_path)\n\n # Save PDF of trees for publication\n # Aspect ratio set in the plot doesn't work for export (unless using the png export on the graph itself), so set the size manually.\n phyla_html_path = str(viz_path / \"phyla_ToL_tree.pdf\")\n king_html_path = str(viz_path / \"kingdom_ToL_tree.pdf\")\n fig_phyla.write_image(phyla_html_path, width=900, height=450)\n fig_kingdom.write_image(king_html_path, width=900, height=450)\n\n\ndef main(src_csv, dest_dir):\n # Check CSV compatibility\n try:\n print(\"Reading CSV\")\n df = pd.read_csv(src_csv, low_memory=False)\n except Exception as e:\n sys.exit(e)\n\n # Check for columns\n print(\"Processing data\")\n missing_cols = []\n for col in TAXA:\n if col not in list(df.columns):\n missing_cols.append(col)\n if len(missing_cols) > 0:\n sys.exit(f\"Source CSV does not have {missing_cols} columns.\")\n\n # If split column included, remove \"train_small\" entries as they are duplicates from \"train\".\n if \"split\" in list(df.columns):\n df = df.loc[df.split != \"train_small\"]\n\n # Check filepath given by user\n dest_dir_path = Path(dest_dir)\n if not dest_dir_path.is_absolute():\n # Use bioclip as reference folder\n base_path = Path(__file__).parent.parent.parent.resolve()\n viz_path = base_path / dest_dir_path\n else:\n viz_path = dest_dir_path\n\n viz_path.mkdir(parents=True, exist_ok=True)\n\n # Make and save trees to chosen filepath\n save_trees(df[TAXA], viz_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=\"Make taxonomic hierarchy visualizations\"\n )\n parser.add_argument(\"input_file\", help=\"Path to the input CSV file\")\n parser.add_argument(\n \"--output_path\",\n default=\"data/visuals\",\n required=False,\n help=\"Path to the folder for output visualization files\",\n )\n args = parser.parse_args()\n\n main(args.input_file, args.output_path)\n"
},
{
"path": "scripts/get_media_manifest.py",
"content": "import os\nimport re\nimport requests\nimport tarfile\nimport argparse\nimport hashlib\nimport json\nfrom datetime import datetime\nfrom tempfile import TemporaryDirectory\nfrom tqdm import tqdm\n\n'''\nUsage:\n$ python get_media_manifest.py --output_dir /absolute/path/to/output_dir\n$ python get_media_manifest.py --output_dir ../relative/path/to/output_dir\n$ python get_media_manifest.py # saves the files in the current directory\n\nInput (optional): --output_dir /path/to/output_dir\nOutputs: \n- eol_media_manifest_YYYY-MM-DD.csv\n- eol_media_manifest_YYYY-MM-DD_log.json\n\nThis script downloads the EOL media manifest from https://eol.org/data/media_manifest.tgz, extracts the CSV files from the tarball, combines them into a single CSV file, and creates JSON log file.\n\nThe JSON log contains:\n- The start time of the download\n- The MD5 checksum of the temporary TGZ file\n- The MD5 checksum of the saved combined CSV file\n- The path to the saved combined CSV file\n- The column names of the CSV file\n- The total number of entries in the combined CSV file\n\nThe script will create the output directory if it does not exist upon confirmation by the user.\nIf the output directory is not specified, the script will save the combined CSV file and log file in the current directory.\n'''\n\nDOWNLOAD_URL = \"https://eol.org/data/media_manifest.tgz\"\n\n\ndef stream_download_file(file_path):\n start_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n with requests.get(DOWNLOAD_URL, stream=True) as response:\n response.raise_for_status()\n total_size_in_bytes = int(response.headers.get('content-length', 0))\n progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True, desc=\"Downloading\")\n\n with open(file_path, \"wb\") as file:\n for chunk in response.iter_content(chunk_size=8192):\n progress_bar.update(len(chunk))\n file.write(chunk)\n progress_bar.close()\n \n md5_checksum = hashlib.md5(open(file_path, 'rb').read()).hexdigest()\n return start_time, md5_checksum\n\n# Extract the numeric part from the chunked manifest file names\ndef extract_numeric_part(file_name):\n match = re.search(r'\\d+', file_name)\n if match:\n return int(match.group())\n else:\n raise ValueError(f\"No numeric part found in {file_name}\")\n\ndef generate_output_filename(output_dir):\n today = datetime.today().strftime('%Y-%m-%d')\n filename = f\"eol_media_manifest_{today}.csv\"\n # If the file exists in this location, append a number to the filename\n i = 1\n while os.path.exists(os.path.join(output_dir, filename)):\n filename = f\"eol_media_manifest_{today}_{i}.csv\"\n i += 1\n return os.path.join(output_dir, filename)\n\ndef extract_and_combine_csv(tgz_file_path, output_file_path):\n with tarfile.open(tgz_file_path, 'r:gz') as tar:\n sorted_members = sorted(tar.getmembers(), key=lambda member: extract_numeric_part(member.name))\n\n with open(output_file_path, 'w', encoding='utf-8') as output_file:\n headers_written = False\n total_lines = 0\n for member in tqdm(sorted_members, desc=\"Extracting files\"):\n if member.name.startswith(\"media_manifest_\") and member.name.endswith(\".csv\"):\n f = tar.extractfile(member)\n if f:\n lines = f.read().decode('utf-8').splitlines()\n if not headers_written:\n headers = lines[0]\n headers_written = True\n output_file.write('\\n'.join(lines))\n output_file.write('\\n')\n total_lines += len(lines) - 1\n return headers, total_lines\n\ndef create_json_log(log_filename, download_start_time, tgz_md5, csv_md5, output_file_path, headers, total_lines):\n log_data = {\n \"download_start_time\": download_start_time,\n \"temp_tgz_md5\": tgz_md5,\n \"manifest_csv_md5\": csv_md5,\n \"manifest_csv_filepath\": os.path.abspath(output_file_path),\n \"column_names\": headers.split(','),\n \"total_entries_in_media_manifest\": total_lines\n }\n\n with open(log_filename, 'w') as log_file:\n json.dump(log_data, log_file, indent=4)\n\ndef main(output_dir):\n output_dir = os.path.abspath(output_dir)\n if not os.path.exists(output_dir):\n confirm = input(f\"The directory {output_dir} does not exist. Do you want it to be created now? (y/n): \")\n if confirm.lower() != 'y':\n print(\"Directory creation cancelled. Exiting script.\")\n return\n else:\n os.makedirs(output_dir)\n \n output_file_path = generate_output_filename(output_dir)\n base_output_file_name = os.path.splitext(output_file_path)[0]\n log_filename = f\"{base_output_file_name}_log.json\"\n\n\n with TemporaryDirectory() as temp_dir:\n temp_file_path = os.path.join(temp_dir, \"media_manifest.tgz\")\n download_start_time, tgz_md5 = stream_download_file(temp_file_path)\n\n headers, total_lines = extract_and_combine_csv(temp_file_path, output_file_path)\n\n csv_md5 = hashlib.md5(open(output_file_path, 'rb').read()).hexdigest()\n\n create_json_log(log_filename, download_start_time, tgz_md5, csv_md5, output_file_path, headers, total_lines)\n\n print(f\"EOL media manifest: {output_file_path}\")\n print(f\"Log file: {log_filename}\")\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Download, combine, and log EOL media manifest CSV files.\")\n parser.add_argument('--output_dir', type=str, default=\".\",\n help=\"Directory to store the combined CSV file and log file. Defaults to the current directory.\")\n args = parser.parse_args()\n\n main(args.output_dir)\n \n"
},
{
"path": "scripts/get_missing_page_ids.py",
"content": "\"\"\"\nThis script finds page ids in the media_cargo_archive/*.tar that are missing the scientific name.\nWe look in these files for the scientific name:\n\n* data/eol/dh21/taxon.tab\n* data/eol/trait_bank/pages.csv\n* data/eol/provider_ids.csv\n* data/eol/vernacularnames.csv\n* data/eol/missing_page_ids.csv\n\nIt then queries the eol.org HTTP API to get the name and writes them to data/eol/missing_page_ids.csv.\n\"\"\"\n\nimport collections\nimport csv\nimport logging\nimport os\nimport re\nimport time\n\nimport requests\nfrom tqdm import tqdm\n\nfrom imageomics import eol\n\nlog_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\nlogging.basicConfig(level=logging.INFO, format=log_format)\nlogger = logging.getLogger(\"eol-scraper\")\n\nmedia_cargo_archive_map_csv = (\n \"/fs/ess/PAS2136/eol/data/interim/media_cargo_archive_map.csv\"\n)\ntaxon_tab = \"data/eol/dh21/taxon.tab\"\nprovider_ids_csv = \"data/eol/provider_ids.csv\"\npages_csv = \"data/eol/trait_bank/pages.csv\"\nvernacularnames_csv = \"data/eol/vernacularnames.csv\"\nscraped_page_ids_csv = \"data/eol/scraped_page_ids.csv\"\n\n# Page Ids that EOL is missing\nmissing_page_ids_csv = \"data/eol/missing_page_ids.csv\"\n\n\ndef get_scientific_name_from_eol(page_id, *, max_retries=1):\n \"\"\"\n Fetch and process text data\n \"\"\"\n\n for retry in range(max_retries):\n try:\n url = f\"https://eol.org/api/pages/1.0/{page_id}.json\"\n params = {\"taxonomy\": True, \"vetted\": 0}\n\n response = requests.get(url, params=params)\n\n if response.status_code == 200:\n data = response.json()\n return data.get(\"taxonConcept\", {}).get(\n \"scientificName\", \"not provided\"\n )\n\n # 500 error, retry up to a limit in case it's a transient issue.\n if response.status_code == 500:\n # Only retry if we haven't exhausted our retries\n if retry < max_retries - 1:\n logger.info(\n \"HTTP %d error for %s, retrying in %d seconds.\",\n response.status_code,\n url,\n 2**retry,\n )\n time.sleep(2**retry)\n else:\n logger.info(\n \"HTTP %d for page_id %d. Quitting.\", response.status_code, page_id\n )\n return None # Exit if status code isn't 200 or 500\n\n except requests.exceptions.HTTPError:\n if retry < max_retries - 1:\n logger.info(\"Retrying in %d seconds.\", 2**retry)\n time.sleep(2**retry)\n except requests.exceptions.ConnectTimeout:\n if retry < max_retries - 1:\n logger.info(\"Timeout for %s. Retrying in %d seconds.\", url, 2**retry)\n time.sleep(2**retry)\n\n return None\n\n\ndef clean_html(string):\n return re.sub(r\"<.*?>(.*?)\", r\"\\1\", string)\n\n\nif __name__ == \"__main__\":\n if not os.path.isfile(missing_page_ids_csv):\n with open(missing_page_ids_csv, \"w\") as fd:\n fd.write(\"page_id\\n\")\n\n if not os.path.isfile(scraped_page_ids_csv):\n with open(scraped_page_ids_csv, \"w\") as fd:\n fd.write(\"page_id,scientific_name\\n\")\n\n media_page_id_lists = collections.defaultdict(list)\n\n with open(media_cargo_archive_map_csv) as fd:\n reader = csv.reader(fd)\n next(reader) # headers\n for img, _ in tqdm(reader, desc=\"media_cargo_archive_map.csv\"):\n eol_filename = eol.ImageFilename.from_filename(img)\n media_page_id_lists[eol_filename.page_id].append(eol_filename)\n\n assert sum(len(imgs) for imgs in media_page_id_lists.values()) > 6.7e6\n\n missing = set(media_page_id_lists)\n page_id_to_scientific = {}\n\n with open(taxon_tab) as fd:\n reader = csv.DictReader(fd, delimiter=\"\\t\")\n for row in tqdm(reader, desc=\"taxon.tab\"):\n if not row[\"eolID\"] or not row[\"scientificName\"]:\n continue\n page_id = int(row[\"eolID\"])\n if page_id not in page_id_to_scientific:\n page_id_to_scientific[page_id] = row[\"scientificName\"]\n\n missing -= set(page_id_to_scientific)\n print(\n f\"After reading taxon.tab, missing {len(missing)} page ids' scientific names.\"\n )\n\n # provider_ids.csv\n with open(provider_ids_csv) as fd:\n reader = csv.DictReader(fd)\n for row in tqdm(reader, desc=\"provider_ids.csv\"):\n if not row[\"page_id\"] or not row[\"preferred_canonical_for_page\"]:\n continue\n page_id = int(row[\"page_id\"])\n if page_id not in page_id_to_scientific:\n page_id_to_scientific[page_id] = row[\"preferred_canonical_for_page\"]\n\n missing -= set(page_id_to_scientific)\n print(\n f\"After reading provider_ids.csv, missing {len(missing)} page ids' scientific names.\"\n )\n\n # trait_bank/pages.csv\n with open(pages_csv) as fd:\n reader = csv.DictReader(fd)\n for row in tqdm(reader, desc=\"trait_bank/pages.csv\"):\n if not row[\"page_id\"] or not row[\"canonical\"]:\n continue\n page_id = int(row[\"page_id\"])\n if page_id not in page_id_to_scientific:\n page_id_to_scientific[page_id] = row[\"canonical\"]\n\n missing -= set(page_id_to_scientific)\n print(\n f\"After reading trait_bank/pages.csv, missing {len(missing)} page ids' scientific names.\"\n )\n\n # vernacularnames.csv\n with open(vernacularnames_csv) as fd:\n reader = csv.DictReader(fd)\n for row in tqdm(reader, desc=\"vernacularnames.csv\"):\n page_id = int(row[\"page_id\"])\n\n if page_id not in page_id_to_scientific:\n page_id_to_scientific[page_id] = clean_html(row[\"canonical_form\"])\n\n missing -= set(page_id_to_scientific)\n print(\n f\"After reading vernacularnames.csv, missing {len(missing)} page ids' scientific names.\"\n )\n\n # scraped_page_ids.csv is the file we write to when scraping EOL's website\n with open(scraped_page_ids_csv) as fd:\n reader = csv.DictReader(fd)\n for row in tqdm(reader, desc=\"scraped_page_ids.csv\"):\n page_id = int(row[\"page_id\"])\n\n if page_id not in page_id_to_scientific:\n page_id_to_scientific[page_id] = row[\"scientific_name\"]\n\n missing -= set(page_id_to_scientific)\n print(\n f\"After reading scraped_page_ids.csv, missing {len(missing)} page ids' scientific names.\"\n )\n\n reqs_per_sec = 1 # just a guess\n print(f\"Querying eol.org/api/pages/1.0/ for {len(missing)} page ids.\")\n print(\n f\"At {reqs_per_sec} requests/second, this will take {len(missing) / reqs_per_sec / 60 / 60:.1f} hours.\"\n )\n\n with open(missing_page_ids_csv) as fd:\n actually_missing = set(int(i) for i in fd.read().split()[1:])\n\n for page_id in tqdm(missing):\n if page_id in actually_missing:\n continue\n\n scientific_name = get_scientific_name_from_eol(page_id)\n if scientific_name is None:\n # logger.info(\"Couldn't find scientific name for page_id %d\", page_id)\n with open(missing_page_ids_csv, \"a\") as fd:\n fd.write(f\"{page_id}\\n\")\n else:\n with open(scraped_page_ids_csv, \"a\") as fd:\n fd.write(f\"{page_id},{scientific_name}\\n\")\n"
},
{
"path": "scripts/global_names_resolver.py",
"content": "import requests\nimport json\nfrom tqdm import tqdm\nimport argparse\n'''\nExample usage:\n\npython global_names_resolver.py ../data/resolve-please.jsonl ../data/resolved.jsonl --batch_size 300\n\nNote that performance seems to saturate beyond a batch size of 200-300 names.\n\nThis reads an input JSONL file with the following line format:\n{\n \"genus\": \"Trametes\",\n \"species\": \"trogii\"\n}\n\nAnd outputs a JSONL file with the following line format:\n\n{\n \"Trametes trogii\": {\n \"Open Tree of Life Reference Taxonomy\": {\n \"kingdom\": \"Fungi\",\n \"phylum\": \"Basidiomycota\",\n \"class\": \"Agaricomycetes\",\n \"order\": \"Polyporales\",\n \"family\": \"Polyporaceae\",\n \"genus\": \"Trametes\",\n \"species\": \"Trametes trogii\"\n }\n }\n}\n\nThe output contains the search term used, the name of the provider for the highest scoring result, and the 7-rank hierarchy.\n'''\n\n# Function to resolve a batch of names and return structured data\ndef resolve_names(names):\n endpoint = 'http://resolver.globalnames.org/name_resolvers.json'\n params = {\n 'names': '|'.join(names),\n 'with_canonical_ranks': 'true'\n }\n response = requests.get(endpoint, params=params)\n if response.status_code != 200:\n return None\n\n data = response.json()\n structured_data_batch = {}\n\n for item_data in data['data']: \n if 'results' not in item_data: \n continue \n\n highest_score = 0\n highest_score_data = {}\n for result in item_data['results']:\n score = result.get('score', 0)\n ranks = result.get('classification_path_ranks', '')\n desired_ranks = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']\n if all(rank in ranks.lower() for rank in desired_ranks):\n classification_path = result.get('classification_path', '')\n data_source_title = result.get('data_source_title', '')\n path_list = classification_path.split('|')\n rank_list = ranks.lower().split('|')\n if len(path_list) != len(rank_list):\n continue\n rank_dict = {rank: term for rank, term in zip(rank_list, path_list) if rank in desired_ranks}\n if score > highest_score:\n highest_score = score\n highest_score_data = {data_source_title: rank_dict}\n name = item_data['supplied_name_string']\n if highest_score > 0:\n structured_data_batch[name] = highest_score_data\n\n return structured_data_batch\n\ndef main(input_file_path, output_file_path, batch_size=300):\n # Get total number of lines in the input file for tqdm\n with open(input_file_path, 'r') as infile:\n total_lines = sum(1 for line in infile)\n\n # Read input file and loop through each line\n with open(input_file_path, 'r') as infile:\n with open(output_file_path, 'w') as outfile:\n names_batch = []\n for line in tqdm(infile, total=total_lines, unit=\"line\"):\n entry = json.loads(line)\n name = f\"{entry['genus']} {entry['species']}\"\n names_batch.append(name)\n if len(names_batch) == batch_size:\n structured_data_batch = resolve_names(names_batch)\n if structured_data_batch:\n for name, data in structured_data_batch.items():\n outfile.write(json.dumps({name: data}) + '\\n')\n outfile.flush()\n names_batch = [] # Reset batch\n\n # Last batch if it has fewer names than the specified batch size\n if names_batch:\n structured_data_batch = resolve_names(names_batch)\n if structured_data_batch:\n for name, data in structured_data_batch.items():\n outfile.write(json.dumps({name: data}) + '\\n')\n outfile.flush()\n\n print(\"Data resolution complete.\")\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Resolve species names to structured data')\n parser.add_argument('input_file', help='Path to the input JSONL file')\n parser.add_argument('output_file', help='Path to the output JSONL file')\n parser.add_argument('--batch_size', type=int, default=300, help='Number of names to process in each batch')\n\n args = parser.parse_args()\n \n main(args.input_file, args.output_file, args.batch_size)\n\n"
},
{
"path": "scripts/inat21_to_wds.py",
"content": "\"\"\"\nConverts iNat21 to a pretraining webdataset format.\n\n* Only includes 9K training classes so there are 1K unseen classes for evaluation\n* Uses both scientific and common names in the text descriptions\n\"\"\"\nimport os\n\nimport torchvision\nimport webdataset\nfrom tqdm.auto import tqdm\n\nimport evaluation.data\nimport imageomics.naming\n\ninat_root = \"/local/scratch/cv_datasets/inat21/raw\"\nsplit = \"train\"\nresize_size = (224, 224)\n\noutput_root = \"/local/scratch/cv_datasets/inat21/pretrain-webdataset\"\n\n# Pattern for shard names\nshard_pattern = \"shard-%06d.tar\"\nmax_size = 1e9\n\n# Make the output directory\noutput_root = os.path.join(output_root, split, \"shards\")\nos.makedirs(output_root, exist_ok=True)\n\n\ndef parse(raw):\n index, *tiers = raw.split(\"_\")\n return int(index, base=10), tiers\n\n\ndef make_descriptions(classname):\n index, tiers = parse(classname)\n scientific_name = \" \".join(tiers)\n yield f\"a photo of {scientific_name}.\"\n\n if index not in common_name_lookup:\n # Don't know the common name\n return\n\n common_name = common_name_lookup[index]\n yield f\"a photo of {common_name}.\"\n\n\nif __name__ == \"__main__\":\n # Set up common name lookup\n common_name_lookup = imageomics.naming.read_mapping()\n\n dataset = evaluation.data.PretrainingInat(\n os.path.join(inat_root, split),\n transform=torchvision.transforms.Resize(resize_size, antialias=True),\n )\n\n sink = webdataset.ShardWriter(\n os.path.join(output_root, shard_pattern), maxsize=max_size\n )\n\n for i, (image, cls) in enumerate(tqdm(dataset)):\n label = parse(dataset.classes[cls])\n for desc in make_descriptions(dataset.classes[cls]):\n sink.write({\"__key__\": f\"sample{i:08d}\", \"jpg\": image, \"txt\": desc})\n\n sink.close()\n"
},
{
"path": "scripts/inat_common_names.py",
"content": "\"\"\"\nGets a mapping of scientific names to common names for iNat21 dataset\n\"\"\"\n\nimport csv\nimport os\n\nimport imageomics.naming\n\narchive_path = \"data/inaturalist-taxonomy.dwca\"\ntaxa_path = os.path.join(archive_path, \"taxa.csv\")\nenglish_path = os.path.join(archive_path, \"VernacularNames-english.csv\")\ninat_root = \"/local/scratch/cv_datasets/inat21/raw\"\n\n\ndef row_dict_to_taxon(row):\n assert row[\"taxonRank\"] == \"species\"\n return imageomics.naming.Taxon(\n row[\"kingdom\"],\n row[\"phylum\"],\n row[\"class\"],\n row[\"order\"],\n row[\"family\"],\n row[\"genus\"],\n row[\"specificEpithet\"],\n website_id=int(row[\"id\"]),\n )\n\n\ndef load_dataset_classes():\n directory = os.path.join(inat_root, \"val\")\n classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())\n if not classes:\n raise FileNotFoundError(f\"Couldn't find any class folders in {directory}.\")\n assert len(classes) == 10_000, \"iNat21 has 10K classes!\"\n\n return classes\n\n\ndef main():\n website_id_lookup = {}\n # Load all\n with open(taxa_path, \"r\") as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n if row[\"taxonRank\"] != \"species\":\n continue\n taxon = row_dict_to_taxon(row)\n\n assert taxon.website_id not in website_id_lookup\n website_id_lookup[taxon.website_id] = taxon\n\n # Set common names\n with open(english_path, \"r\") as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n website_id = int(row[\"id\"])\n\n if website_id not in website_id_lookup:\n print(f\"missing id {website_id} in taxa.csv\")\n continue\n\n common_name = row[\"vernacularName\"]\n website_id_lookup[website_id].common_name = common_name\n\n # Lookup from scientific name to taxa\n scientific_name_lookup = {}\n for _, taxon in website_id_lookup.items():\n scientific_name_lookup[taxon.scientific_name] = taxon\n\n mapping = {}\n for c in load_dataset_classes():\n taxon = imageomics.naming.dataset_class_to_taxon(c)\n if taxon.scientific_name not in scientific_name_lookup:\n print(f\"missing species {taxon.scientific_name} in taxa.csv\")\n continue\n\n scientific_name_lookup[taxon.scientific_name].dataset_id = taxon.dataset_id\n taxon = scientific_name_lookup[taxon.scientific_name]\n if not taxon.common_name:\n print(f\"missing common name for {taxon.scientific_name}\")\n continue\n\n mapping[taxon.dataset_id] = taxon.common_name\n\n imageomics.naming.write_mapping(mapping)\n\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "scripts/reproduce_tree-of-life-10m.sh",
"content": "#!/bin/bash\n# This script is used to reproduce the TreeOfLife-10M dataset in full\n\n# Set the dataset paths\ndataset_path=\"../data/TreeOfLife-10M\"\nmetadata_path=\"${dataset_path}/metadata\"\nnames_path=\"${metadata_path}/naming\"\neol_root=\"${dataset_path}/dataset/eol\"\ninat21_root=\"${dataset_path}/dataset/inat21\"\nbioscan_root=\"${dataset_path}/dataset/bioscan\"\n\nmkdir -p \"${dataset_path}\" \"${metadata_path}\" \"${names_path}\" \"${eol_root}\" \"${inat21_root}\" \"${bioscan_root}\"\n\n# Use aria2c if Apptainer is available for better download performance \naria2_tag=\"202209060423\"\naria2_sif_path=\"../containers/aria2-pro_${aria2_tag}.sif\"\n\n# Ensure the aria2 container is available\nif command -v apptainer &> /dev/null; then\n if [ ! -f \"$aria2_sif_path\" ]; then\n mkdir -p \"../containers\"\n apptainer build \"$aria2_sif_path\" \"docker://p3terx/aria2-pro:${aria2_tag}\"\n if [ $? -ne 0 ]; then\n echo \"Failed to build the aria2 container. Falling back to wget for downloads.\"\n aria2_sif_path=\"\" # Ensure the download function knows aria2c is unavailable\n fi\n fi\nelse\n echo \"Apptainer is not installed. Falling back to wget for all downloads.\"\n aria2_sif_path=\"\"\nfi\n\n# Download function that uses aria2c if available\ndownload() {\n echo \"Downloading $1 to $2/$3\"\n url=$1\n output_path=$2\n output_file=$3\n\n if [ -n \"$aria2_sif_path\" ] && [ -f \"$aria2_sif_path\" ]; then\n # Try downloading with aria2c\n apptainer exec \"$aria2_sif_path\" aria2c \\\n --dir=\"$output_path\" \\\n --out=\"$output_file\" \\\n --continue \\\n --max-connection-per-server=16 \\\n \"$url\"\n if [ $? -eq 0 ]; then\n return 0\n fi\n fi\n\n # Fallback to wget if aria2c fails or is not available\n wget -c -O \"${output_path}/${output_file}\" \"$url\"\n}\n\n###TESTOUTFORBIOSCAN\n# # Download TOL-10M metadata from Hugging Face\n# hf_metadata_url_prefix = \"https://huggingface.co/datasets/imageomics/TreeOfLife-10M/resolve/main/metadata/\"\n\n# download \"${hf_metadata_url_prefix}catalog.csv?download=true\" \"$metadata_path\" \"catalog.csv\"\n# download \"${hf_metadata_url_prefix}licenses.csv?download=true\" \"$metadata_path\" \"licenses.csv\"\n# download \"${hf_metadata_url_prefix}species_level_taxonomy_chains.csv?download=true\" \"$metadata_path\" \"species_level_taxonomy_chains.csv\"\n# download \"${hf_metadata_url_prefix}species_level_taxonomy.csv?download=true\" \"$metadata_path\" \"species_level_taxonomy.csv\"\n\n# download \"${hf_metadata_url_prefix}bioscan_name_lookup.json?download=true\" \"$names_path\" \"bioscan_name_lookup.json\"\n# download \"${hf_metadata_url_prefix}inat21_name_lookup.json?download=true\" \"$names_path\" \"inat21_name_lookup.json\"\n# download \"${hf_metadata_url_prefix}eol_name_lookup.json?download=true\" \"$names_path\" \"eol_name_lookup.json\"\n\n# Download the EOL portion from Hugging Face\nfor i in $(seq -w 22 63); do\n file=\"image_set_${i}.tar.gz\"\n url=\"https://huggingface.co/datasets/imageomics/TreeOfLife-10M/resolve/main/dataset/EOL/${file}?download=true\"\n\n download \"$url\" \"$eol_root\" \"$file\"\ndone\n\n# # Download the iNat21 portion\n# download \"https://ml-inat-competition-datasets.s3.amazonaws.com/2021/train.tar.gz\" \"$inat21_root\" \"train.tar.gz\"\n# tar -xzf \"${inat21_root}/train.tar.gz\" -C \"$inat21_root\" && rm \"${inat21_root}/train.tar.gz\" &\n###TESTOUTFORBIOSCAN\n\n# Download the BioSCAN-1M portion\nbioscan_url=\"https://zenodo.org/record/8030065/files/cropped_256.zip?download=1\"\necho \"Downloading BioSCAN-1M\"\ndownload \"https://zenodo.org/records/8030065/files/cropped_256.zip?download=1\" \"$bioscan_root\" \"cropped_256.zip\"\n\n####################\n##EITHERTHIS\n# unzip -q \"${bioscan_root}/cropped_256.zip\" -d \"$bioscan_root\" && rm \"${bioscan_root}/cropped_256.zip\" &\n##EITHERTHIS\n\n##ORTHIS\n# Extract to a temporary directory\necho \"Extracting BioSCAN-1M to $bioscan_root\"\ntemp_extraction_path=\"${bioscan_root}/temp\"\nmkdir -p \"$temp_extraction_path\"\nunzip -q \"${bioscan_root}/cropped_256.zip\" -d \"$temp_extraction_path\" && \\\nmv \"${temp_extraction_path}/bioscan/images/cropped_256/\"* \"${bioscan_root}/\" && \\\nrm -r \"$temp_extraction_path\"\n\n# Rename the directories as needed\ncd \"${bioscan_root}\"\nfor dir in part*; do\n new_dir_name=$(echo \"$dir\" | sed 's/part/part/')\n mv \"$dir\" \"$new_dir_name\"\ndone\n\n# Clean up the zip file\nrm \"${bioscan_root}/cropped_256.zip\"\n##ORTHIS\n####################\n\n# /fs/scratch/PAS2136/thompsonmj/bioclip/data/TreeOfLife-10M/dataset/bioscan/bioscan/images/cropped_256/partN\n# https://chatgpt.com/share/053a4e09-a9ad-48e1-b1a6-219421d29aed\n\n"
},
{
"path": "scripts/setup_download_tol-10m_components.bash",
"content": "#!/bin/bash\n\nREPO_ROOT=\"$PWD\"\nSLURM_SUBMIT_DIR=\"$REPO_ROOT/slurm\"\n\nexport REPO_ROOT SLURM_SUBMIT_DIR\n\n# IF YOU WOULD LIKE TO SET A CUSTOM DATASET PATH OR SLURM DIRECTIVES, CHANGE THE VARIABLES BELOW\n\n\n####################################### customize-below >>>\n\n\n# optional:\n# Set a custom path for the dataset. \n# It defaults to the location below within the git repository.\n# If you leave the default location, ensure you have placed the repository in a location with sufficient space.\ndataset_path=\"${REPO_ROOT}/data/TreeOfLife-10M\"\n\nSLURM_NODES=1\n\n# SLURM directives for each job\n# Wall times are set conservatively to ensure jobs complete on most systems\n\n# Quick download of metadata files\nMETADATA_SLURM_TIME=\"0:20:00\"\nMETADATA_SLURM_NTASKS_PER_NODE=1\nMETADATA_SLURM_OUTPUT=\"${REPO_ROOT}/logs/download_metadata_%j.out\"\nMETADATA_SLURM_ERROR=\"${REPO_ROOT}/logs/download_metadata_%j.err\"\n\n# Network-intenstive download of 63x ~30GB .tar.gz files with EOL images (~2.5h on OSC, 200-300MiB/s)\n# Wall time may need to increase if `wget` is used instead of `aria2c`\nEOL_SLURM_TIME=\"18:00:00\"\nEOL_SLURM_NTASKS_PER_NODE=12\nEOL_SLURM_OUTPUT=\"${REPO_ROOT}/logs/download_eol_%j.out\"\nEOL_SLURM_ERROR=\"${REPO_ROOT}/logs/download_eol_%j.err\"\n\n# Network intensive download (~11min on OSC, 340MiB/s), CPU-bound extraction (~88min total on OSC)\nINAT21_SLURM_TIME=\"4:00:00\"\nINAT21_SLURM_NTASKS_PER_NODE=1\nINAT21_SLURM_OUTPUT=\"${REPO_ROOT}/logs/download_inat21_%j.out\"\nINAT21_SLURM_ERROR=\"${REPO_ROOT}/logs/download_inat21_%j.err\"\n\n# Modest download, CPU-bound extraction (made fast by parallelization) (~8min on OSC)\nBIOSCAN_SLURM_TIME=\"2:00:00\"\nBIOSCAN_SLURM_NTASKS_PER_NODE=20\nBIOSCAN_SLURM_OUTPUT=\"${REPO_ROOT}/logs/download_bioscan_%j.out\"\nBIOSCAN_SLURM_ERROR=\"${REPO_ROOT}/logs/download_bioscan_%j.err\"\n\n\n####################################### customize-above <<<\n\n\nexport SLURM_ACCOUNT SLURM_NODES\nexport METADATA_SLURM_TIME METADATA_SLURM_NTASKS_PER_NODE METADATA_SLURM_OUTPUT METADATA_SLURM_ERROR\nexport EOL_SLURM_TIME EOL_SLURM_NTASKS_PER_NODE EOL_SLURM_OUTPUT EOL_SLURM_ERROR\nexport INAT21_SLURM_TIME INAT21_SLURM_NTASKS_PER_NODE INAT21_SLURM_OUTPUT INAT21_SLURM_ERROR\nexport BIOSCAN_SLURM_TIME BIOSCAN_SLURM_NTASKS_PER_NODE BIOSCAN_SLURM_OUTPUT BIOSCAN_SLURM_ERROR\n\nmetadata_path=\"${dataset_path}/metadata\"\nnames_path=\"${metadata_path}/naming\"\neol_root=\"${dataset_path}/dataset/eol\"\ninat21_root=\"${dataset_path}/dataset/inat21\"\nbioscan_root=\"${dataset_path}/dataset/bioscan\"\nrarespecies_root=\"${dataset_path}/dataset/rarespecies\"\ncontainer_path=\"${REPO_ROOT}/containers\"\nlogs_path=\"${REPO_ROOT}/logs\"\n\nexport dataset_path metadata_path names_path eol_root inat21_root bioscan_root rarespecies_root container_path logs_path\n\n# Create directories\nmkdir -p \"${dataset_path}\" \"${metadata_path}\" \"${names_path}\" \"${eol_root}\" \"${inat21_root}\" \"${bioscan_root}\" \"${rarespecies_root}\" \"${container_path}\" \"${logs_path}\"\n\nis_slurm_job() {\n [ -n \"$SLURM_JOB_ID\" ]\n}\n\n# Download function for fast download with aria2c if Apptainer is available, otherwise uses wget\ndownload() {\n echo \"Downloading $1 to $2/$3\"\n url=$1\n output_path=$2\n output_file=$3\n\n if [ -n \"$aria2_sif_path\" ] && [ -f \"$aria2_sif_path\" ]; then\n apptainer exec \"$aria2_sif_path\" aria2c \\\n --dir=\"$output_path\" \\\n --out=\"$output_file\" \\\n --continue \\\n --max-connection-per-server=16 \\\n \"$url\"\n if [ $? -eq 0 ]; then\n return 0\n fi\n fi\n\n wget -c -O \"${output_path}/${output_file}\" \"$url\"\n}\n\nexport -f download\n\n# Function to extract BIOSCAN-1M\nextract_bioscan() {\n local zip_file=\"$1\"\n local output_dir=\"$2\"\n local temp_dir=\"${output_dir}/temp\"\n local final_file_count=1128313\n\n mkdir -p \"$temp_dir\"\n\n # Adapt resource usage based on environment being used\n local available_cores=$(nproc)\n if is_slurm_job; then\n # Use at least 1\n local requested_tasks=${BIOSCAN_SLURM_NTASKS_PER_NODE:-1}\n requested_tasks=$(( requested_tasks < 1 ? 1 : requested_tasks ))\n \n # Use the smaller of number of cores requested vs available\n local num_tasks=$(( requested_tasks < available_cores ? requested_tasks : available_cores ))\n else\n # For command line, use all available cores up to a maximum of 4\n local num_tasks=$(( available_cores < 4 ? available_cores : 4 ))\n fi\n\n # Extract all parts in parallel\n total_parts=$(unzip -Z1 \"$zip_file\" | grep 'bioscan/images/cropped_256/part' | cut -d'/' -f4 | sort -u | wc -l)\n current_part=0\n # Prevent race to create temp parent directories during unzip\n mkdir -p \"$temp_dir/bioscan/images/cropped_256/\"\n unzip -Z1 \"$zip_file\" | grep 'bioscan/images/cropped_256/part' | cut -d'/' -f4 | sort -u | \\\n xargs -P \"$num_tasks\" -I {} sh -c '\n unzip -q \"$0\" \"bioscan/images/cropped_256/{}/*\" -d \"$1\"\n echo \"Extracted part {}\"\n ' \"$zip_file\" \"$temp_dir\"\n\n # Move all part directories to the final location\n find \"$temp_dir/bioscan/images/cropped_256/\" -type d -name 'part*' -print0 | xargs -0 -I {} mv {} \"$output_dir/\"\n\n # Remove the temp directory\n rm -rf \"$temp_dir\"\n\n # Count the number of extracted files and verify\n local extracted_file_count\n extracted_file_count=$(find \"$output_dir\" -type f -name '*.jpg' | wc -l)\n \n if [ \"$extracted_file_count\" -ne \"$final_file_count\" ]; then\n echo \"Error: Expected $final_file_count files, but found $extracted_file_count files.\" >&2\n return 1\n fi\n\n rm \"$zip_file\"\n\n echo \"Extracted BIOSCAN-1M to $output_dir\"\n}\n\nexport -f extract_bioscan\n\n# Function to extract iNat21\nextract_inat21() {\n local tar_archive=\"$1\"\n local output_dir=\"$2\"\n\n tar -xzf \"$tar_archive\" -C \"$output_dir\"\n\n rm \"$tar_archive\"\n echo \"Extracted iNat21 to $output_dir\"\n}\n\nexport -f extract_inat21\n\n# Container setup for aria2c if Apptainer is available\naria2_tag=\"202209060423\"\naria2_sif_path=\"${container_path}/aria2-pro_${aria2_tag}.sif\"\n\nif command -v apptainer &> /dev/null; then\n mkdir -p \"$container_path\"\n if [ ! -f \"$aria2_sif_path\" ]; then\n apptainer build \"$aria2_sif_path\" \"docker://p3terx/aria2-pro:${aria2_tag}\"\n if [ $? -ne 0 ]; then\n echo \"Failed to build the aria2 container. Falling back to wget for downloads.\"\n aria2_sif_path=\"\"\n fi\n fi\nelse\n echo \"Apptainer is not installed. Falling back to wget for all downloads.\"\n aria2_sif_path=\"\"\nfi\n\nexport aria2_sif_path\n"
},
{
"path": "scripts/submit_download_tol-10m_components.bash",
"content": "#!/bin/bash\n\nset -ex\n\n# Run from the root of the repository.\n# Usage: sbatch --account scripts/submit_download_tol-10m_components.bash\n\n# Source the setup script\nsource \"scripts/setup_download_tol-10m_components.bash\"\nexport SBATCH_ACCOUNT=$SLURM_JOB_ACCOUNT # Applies to all child jobs\n\n# Ensure necessary directories exist\nmkdir -p \"$logs_path\"\n\n# Submit jobs\nmetadata_job=$(sbatch \\\n --time=$METADATA_SLURM_TIME \\\n --nodes=$SLURM_NODES \\\n --ntasks-per-node=$METADATA_SLURM_NTASKS_PER_NODE \\\n --output=$METADATA_SLURM_OUTPUT \\\n --error=$METADATA_SLURM_ERROR \\\n \"$SLURM_SUBMIT_DIR/download_metadata.slurm\" | awk '{print $4}')\n\neol_job=$(sbatch \\\n --time=$EOL_SLURM_TIME \\\n --nodes=$SLURM_NODES \\\n --ntasks-per-node=$EOL_SLURM_NTASKS_PER_NODE \\\n --output=$EOL_SLURM_OUTPUT \\\n --error=$EOL_SLURM_ERROR \\\n \"$SLURM_SUBMIT_DIR/download_eol.slurm\" | awk '{print $4}')\n\ninat21_job=$(sbatch \\\n --time=$INAT21_SLURM_TIME \\\n --nodes=$SLURM_NODES \\\n --ntasks-per-node=$INAT21_SLURM_NTASKS_PER_NODE \\\n --output=$INAT21_SLURM_OUTPUT \\\n --error=$INAT21_SLURM_ERROR \\\n \"$SLURM_SUBMIT_DIR/download_inat21.slurm\" | awk '{print $4}')\n\nbioscan_job=$(sbatch \\\n --time=$BIOSCAN_SLURM_TIME \\\n --nodes=$SLURM_NODES \\\n --ntasks-per-node=$BIOSCAN_SLURM_NTASKS_PER_NODE \\\n --output=$BIOSCAN_SLURM_OUTPUT \\\n --error=$BIOSCAN_SLURM_ERROR \\\n \"$SLURM_SUBMIT_DIR/download_bioscan.slurm\" | awk '{print $4}')\n\necho \"Submitted jobs: metadata ($metadata_job), EOL ($eol_job), iNat21 ($inat21_job), BIOSCAN ($bioscan_job)\"\n\necho \"Repo root: $REPO_ROOT\"\n"
},
{
"path": "setup.py",
"content": "\"\"\" Setup\n\"\"\"\nfrom setuptools import setup, find_packages\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\n# Get the long description from the README file\nwith open(path.join(here, 'README.md'), encoding='utf-8') as f:\n long_description = f.read()\n\ndef _read_reqs(relpath):\n fullpath = path.join(path.dirname(__file__), relpath)\n with open(fullpath) as f:\n return [s.strip() for s in f.readlines() if (s.strip() and not s.startswith(\"#\"))]\n\nREQUIREMENTS = _read_reqs(\"requirements.txt\")\nTRAINING_REQUIREMENTS = _read_reqs(\"requirements-training.txt\")\n\nexec(open('src/open_clip/version.py').read())\nsetup(\n name='open_clip_torch',\n version=__version__,\n description='OpenCLIP',\n long_description=long_description,\n long_description_content_type='text/markdown',\n url='https://github.com/mlfoundations/open_clip',\n author='',\n author_email='',\n classifiers=[\n # How mature is this project? Common values are\n # 3 - Alpha\n # 4 - Beta\n # 5 - Production/Stable\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Education',\n 'Intended Audience :: Science/Research',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Programming Language :: Python :: 3.9',\n 'Programming Language :: Python :: 3.10',\n 'Topic :: Scientific/Engineering',\n 'Topic :: Scientific/Engineering :: Artificial Intelligence',\n 'Topic :: Software Development',\n 'Topic :: Software Development :: Libraries',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ],\n\n # Note that this is a string of words separated by whitespace, not a list.\n keywords='CLIP pretrained',\n package_dir={'': 'src'},\n packages=find_packages(where='src'),\n include_package_data=True,\n install_requires=REQUIREMENTS,\n extras_require={\n \"training\": TRAINING_REQUIREMENTS,\n },\n python_requires='>=3.7',\n)\n"
},
{
"path": "slurm/check-wds.slurm",
"content": "#!/bin/bash\n#SBATCH --job-name=check_wds\n#SBATCH --output=logs/check_wds_%j.out\n#SBATCH --error=logs/check_wds_%j.err\n#SBATCH --time=12:00:00\n#SBATCH --nodes=1\n\n# Usage:\n# sbatch --account ACCOUNT --cpus-per-task N slurm/check-wds.slurm SHARDS \n# e.g.\n# sbatch --account ABC123 --cpus-per-task 32 slurm/check-wds.slurm 'data/TreeOfLife-10M/dataset/evobio10m-CVPR-2024/224x224/train/shard-{000000..000165}.tar'\n\nmodule load miniconda3/24.1.2-py310 # Adjust as needed\n\nconda activate bioclip-train\n\nshardlist=$1\n\nif [ -z \"$shardlist\" ]; then\n echo \"Shard list is required. Usage: sbatch --account ACCOUNT slurm/check_wds.slurm SHARDS\"\n exit 1\nfi\n\nsrun python scripts/evobio10m/check_wds.py --shardlist \"$shardlist\" --workers $SLURM_CPUS_PER_TASK > logs/bad-shards.txt\n"
},
{
"path": "slurm/download_bioscan.slurm",
"content": "#!/bin/bash\n\nset -ex\ntime_start=$(date +%s)\n\n# Source setup script\nsource \"$SLURM_SUBMIT_DIR/scripts/setup_download_tol-10m_components.bash\"\n\n# source \"scripts/setup_download_tol-10m_components.bash\" # Use this for troubleshooting or running this component independently\n\n# Download BIOSCAN data\nbioscan_url=\"https://zenodo.org/record/8030065/files/cropped_256.zip?download=1\"\ndownload \"$bioscan_url\" \"$bioscan_root\" \"cropped_256.zip\"\n\n# Extract BIOSCAN archive\nzip_archive=\"${bioscan_root}/cropped_256.zip\"\nextract_bioscan \"$zip_archive\" \"$bioscan_root\"\n\n# End timing\ntime_end=$(date +%s)\ntime_elapsed=$((time_end - time_start))\n\n# Notify completion\necho \"Total execution time: $time_elapsed seconds\" > \"${logs_path}/download_bioscan_completed.txt\"\n"
},
{
"path": "slurm/download_eol.slurm",
"content": "#!/bin/bash\n\nset -ex\ntime_start=$(date +%s)\n\n# Source setup script\nsource \"$SLURM_SUBMIT_DIR/scripts/setup_download_tol-10m_components.bash\"\n\n# source \"scripts/setup_download_tol-10m_components.bash\" # Use this for troubleshooting or running this component independently\n\n# Download EOL data\nfor i in $(seq -w 01 63); do\n file=\"image_set_${i}.tar.gz\"\n url=\"https://huggingface.co/datasets/imageomics/TreeOfLife-10M/resolve/main/dataset/EOL/${file}?download=true\"\n download \"$url\" \"$eol_root\" \"$file\"\ndone\n\n# End timing\ntime_end=$(date +%s)\ntime_elapsed=$((time_end - time_start))\n\n# Notify completion\necho \"Total execution time: $time_elapsed seconds\" > \"${logs_path}/download_eol_completed.txt\"\n"
},
{
"path": "slurm/download_inat21.slurm",
"content": "#!/bin/bash\n\nset -ex\ntime_start=$(date +%s)\n\n# Source setup script\nsource \"$SLURM_SUBMIT_DIR/scripts/setup_download_tol-10m_components.bash\"\n\n# source \"scripts/setup_download_tol-10m_components.bash\" # Use this for troubleshooting or running this component independently\n\n# Download iNat21 data\ninat21_url=\"https://ml-inat-competition-datasets.s3.amazonaws.com/2021/train.tar.gz\"\ndownload \"$inat21_url\" \"$inat21_root\" \"train.tar.gz\"\n\n# Extract iNat21 archive\ntar_archive=\"${inat21_root}/train.tar.gz\"\nextract_inat21 \"$tar_archive\" \"$inat21_root\"\n\n# End timing\ntime_end=$(date +%s)\ntime_elapsed=$((time_end - time_start))\n\n# Notify completion\necho \"Total execution time: $time_elapsed seconds\" > \"${logs_path}/download_inat21_completed.txt\"\n"
},
{
"path": "slurm/download_metadata.slurm",
"content": "#!/bin/bash\n\nset -ex\ntime_start=$(date +%s)\n\n# Source setup script\nsource \"$SLURM_SUBMIT_DIR/scripts/setup_download_tol-10m_components.bash\"\n\n# source \"scripts/setup_download_tol-10m_components.bash\" # Use this for troubleshooting or running this component independently\n\n# Download metadata\nhf_metadata_url_prefix=\"https://huggingface.co/datasets/imageomics/TreeOfLife-10M/resolve/main/metadata/\"\nhf_naming_url_prefix=\"https://huggingface.co/datasets/imageomics/TreeOfLife-10M/resolve/main/metadata/naming/\"\n\n# Define URLs and output paths\nurls_and_paths=(\n \"${hf_metadata_url_prefix}catalog.csv?download=true ${metadata_path}/catalog.csv\"\n \"${hf_metadata_url_prefix}licenses.csv?download=true ${metadata_path}/licenses.csv\"\n \"${hf_metadata_url_prefix}mapping.sqlite?download=true ${metadata_path}/mapping.sqlite\"\n \"${hf_metadata_url_prefix}taxon.tab?download=true ${metadata_path}/taxon.tab\"\n \"${hf_metadata_url_prefix}species_level_taxonomy_chains.csv?download=true ${metadata_path}/species_level_taxonomy_chains.csv\"\n \"${hf_naming_url_prefix}bioscan_name_lookup.json?download=true ${names_path}/bioscan_name_lookup.json\"\n \"${hf_naming_url_prefix}inat21_name_lookup.json?download=true ${names_path}/inat21_name_lookup.json\"\n \"${hf_naming_url_prefix}eol_name_lookup.json?download=true ${names_path}/eol_name_lookup.json\"\n)\n\n# Download files\nfor item in \"${urls_and_paths[@]}\"; do\n read -r url output_path <<< \"$item\"\n output_dir=$(dirname \"$output_path\")\n output_file=$(basename \"$output_path\")\n download \"$url\" \"$output_dir\" \"$output_file\"\ndone\n\n# End timing\ntime_end=$(date +%s)\ntime_elapsed=$((time_end - time_start))\n\n# Notify completion\necho \"Total execution time: $time_elapsed seconds\" > \"${logs_path}/download_metadata_completed.txt\"\n"
},
{
"path": "slurm/make-catalog_reproduce.slurm",
"content": "#!/bin/bash\n#SBATCH --job-name=make_catalog_%j \n#SBATCH --output=logs/make_catalog_%j.out\n#SBATCH --error=logs/make_catalog_%j.err\n#SBATCH --time=8:00:00\n#SBATCH --nodes=1\n\n# Usage:\n# sbatch --account --cpus-per-task slurm/make-catalog_reproduce.slurm \\\n# --dir \\\n# --db \\\n# --tag \\\n# --batch-size \n# \n# e.g.\n# \n# sbatch --account ABC123 --cpus-per-task 32 slurm/make-catalog_reproduce.slurm \\\n# --dir data/TreeOfLife-10M/dataset/evobio10m-CVPR-2024/224x224 \\\n# --db data/TreeOfLife-10M/metadata/mapping.sqlite \\\n# --tag CVPR-2024 \\\n# --batch-size 256\n\nmodule load miniconda3/24.1.2-py310 # Adjust as needed\n\nconda activate bioclip-train\n\npython scripts/evobio10m/make_catalog_reproduce.py --workers \"$SLURM_CPUS_PER_TASK\" \"$@\"\n"
},
{
"path": "slurm/make-dataset-mapping.sh",
"content": "#!/bin/bash\n#SBATCH --time=03:00:00\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=48\n#SBATCH --account=PAS2136\n\necho $SLURM_JOB_NAME\n\nsource $HOME/projects/open_clip/pitzer-venv/bin/activate\n\npython scripts/evobio10m/make_mapping.py --tag v2 --workers 48\n"
},
{
"path": "slurm/make-dataset-wds.sh",
"content": "#!/bin/bash\n#SBATCH --time=08:00:00\n#SBATCH --nodes=1\n#SBATCH --ntasks=1\n#SBATCH --cpus-per-task=32\n#SBATCH --account=PAS2136\n#SBATCH --partition=serial\n\necho $SLURM_JOB_NAME\n\nsource $HOME/projects/open_clip/pitzer-venv/bin/activate\n\npython scripts/evobio10m/make_wds.py --tag v3.3-cross-entropy --split val --workers $SLURM_CPUS_PER_TASK\npython scripts/evobio10m/make_wds.py --tag v3.3-cross-entropy --split train_small --workers $SLURM_CPUS_PER_TASK\n# python scripts/evobio10m/make_wds.py --tag v3.3-cross-entropy --split train --workers $SLURM_CPUS_PER_TASK\n"
},
{
"path": "slurm/make-dataset-wds_reproduce.sh",
"content": "#!/bin/bash\n#SBATCH --time=08:00:00\n#SBATCH --job-name=make-dataset-wds_test\n#SBATCH --output=make-dataset-wds_test-%j.out\n#SBATCH --error=make-dataset-wds_test-%j.err\n#SBATCH --nodes=1\n#SBATCH --ntasks=1\n#SBATCH --cpus-per-task=32\n#SBATCH --partition=serial\n\necho $SLURM_JOB_NAME\n\nmodule load miniconda3/24.1.2-py310 # Use your latest miniconda version\n\nconda activate bioclip-train\n\npython scripts/evobio10m/make_wds_reproduce.py --tag CVPR-2024 --split val --workers $SLURM_CPUS_PER_TASK\npython scripts/evobio10m/make_wds_reproduce.py --tag CVPR-2024 --split train_small --workers $SLURM_CPUS_PER_TASK\npython scripts/evobio10m/make_wds_reproduce.py --tag CVPR-2024 --split train --workers $SLURM_CPUS_PER_TASK\n"
},
{
"path": "slurm/train.sh",
"content": "#!/usr/bin/env bash\n#SBATCH --nodes=1\n#SBATCH --account=PAS1576\n#SBATCH --gpus-per-node=4\n#SBATCH --ntasks-per-node=4\n#SBATCH --partition=gpu\n#SBATCH --job-name=training\n#SBATCH --time=96:00:00\n#SBATCH --mem=800GB\n\nsource $HOME/projects/open_clip/.venv/bin/activate\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\n.venv/bin/torchrun --nproc_per_node 4 -m src.training.main \\\n --save-frequency 1 \\\n --train-data './data/train/shard-{000000..000159}.tar' \\\n --val-data './data/val/shard-{000000..000031}.tar' \\\n --dataset-type 'webdataset' \\\n --pretrained 'openai' \\\n --text_type 'random' \\\n --dataset-resampled \\\n --warmup 1000 \\\n --batch-size 4096 \\\n --accum-freq 1 \\\n --epochs 100 \\\n --workers 8 \\\n --model ViT-B-16 \\\n --log-every-n-steps 1 \\\n --lr 1e-4 \\\n --seed 42 \\\n --local-loss \\\n --gather-with-grad \\\n --grad-checkpointing \\\n --logs '../storage/log/' \\\n"
},
{
"path": "src/evaluation/README.md",
"content": "# Parameters for Evaluation\n\n - `label_filename`: File path of the CSV annotation file under the base folder in `data_root`\n - `text_type`: Text type of annotations for test examples. String, default `asis`.\n - `task_type`: retrieve: retrieve image embeddings from image encoder; eval: evaluate with pickle file containing image embeddings; all: evaluate from scratch which both retrieve image embeddings and evaluate the embeddings in one run. String, options: \"retrieve\", \"eval\", \"all\", default `all`.\n - `nfold`: The number of times of sampling training examples during few-shot. `int`, default 5.\n - `kshot_list`: A list of integers for k in k-shot. Default [1,5].\n - `data_root`: File path of base folder which contains images and a CSV annotation file. String, default ''.\n - `logs`: Where to store logs. Use None or 'none' to avoid storing logs. String, default None.\n - `name`: Optional identifier for the experiment when storing logs. Otherwise use current time. String, default None.\n - `workers`: Number of dataloader workers per GPU. `int` value, default 1.\n - `batch-size`: Batch size per GPU. `int` value, default 64.\n - `precision`: Floating point precision. Options: \"amp\", \"amp_bf16\", \"amp_bfloat16\", \"bf16\", \"fp32\". Default `amp`.\n - `model`: Name of the vision backbone to use. String, default \"ViT-B-16\".\n - `pretrained`: Use a pretrained CLIP model weights with the specified tag or file path. If running `few-shot.py` with `task_type=eval`, use this parameter as the pickle file path. Default \"openai\".\n - `pretrained-image`: Load imagenet pretrained weights for image tower backbone if available. Default False.\n - `image-mean`: Override default image mean value of dataset. `float` value, default None.\n - `image-std`: Override default image std deviation of of dataset. `float` value, default None.\n - `aug-cfg`\n - `force-image-size`: Override default image size. `int` value, default None.\n - `force-quick-gelu`: Force use of QuickGELU activation for non-OpenAI transformer models. Default is False.\n - `force-custom-text`: Force use of CustomTextCLIP model (separate text-tower). Default is False.\n - `torchscript`: `torch.jit.script` the model, also uses `jit` version of OpenAI models if `pretrained=='openai'`. Default is False.\n - `trace`: `torch.jit.trace` the model for inference / eval only. Default is False.\n - `debug`: If true, more information is logged. Default is False.\n - `no-set-device-rank`: Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc). Default is False.\n - `seed`: Default random seed, defaults to 0.\n"
},
{
"path": "src/evaluation/__init__.py",
"content": ""
},
{
"path": "src/evaluation/create_final_figure.py",
"content": "import os\n\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import chi2\n\nfrom evaluation.hierarchy_tree_image import (\n TAXONOMICAL_RANKS,\n _get_hierarchy_lbl_map,\n get_colors,\n mahalanobis,\n)\n\n\ndef create_final_image(\n openai_features,\n openai_labels,\n bioclip_features,\n bioclip_labels,\n output=\"tmp/final\",\n top_k=6,\n dpi=100,\n):\n os.makedirs(output, exist_ok=True)\n\n base_colors = get_colors()\n\n nrows = 4\n ncols = 3\n height = 7\n width = 5\n final_fig = plt.figure(figsize=(height * nrows, width * ncols), dpi=dpi)\n final_spec = gridspec.GridSpec(2, 1)\n row1_spec = gridspec.GridSpecFromSubplotSpec(\n 2, 3, subplot_spec=final_spec[0], wspace=0.05\n )\n row2_spec = gridspec.GridSpecFromSubplotSpec(\n 2, 3, subplot_spec=final_spec[1], wspace=0.05\n )\n for lvl in range(1, len(openai_labels)):\n oa_feats = openai_features[lvl]\n oa_lbls = openai_labels[lvl]\n bio_feats = bioclip_features[lvl]\n\n lbl_lengths = []\n sorted_lbls = sorted(list(set(oa_lbls[:, lvl])))\n for lbl in sorted_lbls:\n idx = oa_lbls[:, lvl] == lbl\n lbl_lengths.append([lbl, len(oa_feats[idx])])\n lbl_lengths = sorted(lbl_lengths, key=lambda x: x[1], reverse=True)\n\n row = (lvl - 1) // ncols\n col = (lvl - 1) % ncols\n\n if row == 0:\n bio_ax = final_fig.add_subplot(row1_spec[0, col])\n oa_ax = final_fig.add_subplot(row1_spec[1, col])\n else:\n bio_ax = final_fig.add_subplot(row2_spec[0, col])\n oa_ax = final_fig.add_subplot(row2_spec[1, col])\n\n plt.setp(bio_ax.spines.values(), lw=3, color=\"black\") # Set border width\n plt.setp(oa_ax.spines.values(), lw=3, color=\"black\") # Set border width\n\n highest_preceding_rank = oa_lbls[0, lvl - 1].split(\"_\")[-1]\n fig_title = TAXONOMICAL_RANKS[lvl]\n fig_title += f\" of {highest_preceding_rank}\"\n bio_ax.set_title(f\"(B) {fig_title}\", fontsize=25, y=1.02)\n oa_ax.set_title(f\"(O) {fig_title}\", fontsize=25, y=1.02)\n\n c = 0\n for lbl in sorted_lbls:\n if top_k > 0:\n if lbl not in np.array(lbl_lengths)[:top_k, 0]:\n continue\n idx = oa_lbls[:, lvl] == lbl\n bio_feat = bio_feats[idx]\n oa_feat = oa_feats[idx]\n name = f\"{lbl.split('_')[-1]}\"\n\n mah_dist = mahalanobis(bio_feat)\n p = 1 - chi2.cdf(mah_dist, bio_feat.shape[1] - 1)\n filtered_idx = p >= 0.001\n bio_plot_feat = bio_feat[filtered_idx]\n\n mah_dist = mahalanobis(oa_feat)\n p = 1 - chi2.cdf(mah_dist, oa_feat.shape[1] - 1)\n filtered_idx = p >= 0.001\n oa_plot_feat = oa_feat[filtered_idx]\n\n markersize = 2\n if lvl == 5:\n markersize *= 2\n elif lvl == 6:\n markersize *= 4\n bio_ax.scatter(\n bio_plot_feat[:, 0],\n bio_plot_feat[:, 1],\n label=name,\n color=base_colors[c],\n alpha=0.50,\n s=markersize,\n rasterized=True,\n ) # have to rasterize for .pdf to load quicker\n oa_ax.scatter(\n oa_plot_feat[:, 0],\n oa_plot_feat[:, 1],\n label=name,\n color=base_colors[c],\n alpha=0.50,\n s=markersize,\n rasterized=True,\n ) # have to rasterize for .pdf to load quicker\n\n c += 1\n\n markerscale = 8\n if lvl == 5:\n markerscale = 6\n elif lvl == 6:\n markerscale = 4\n bio_ax.legend(loc=\"upper right\", ncols=2, markerscale=markerscale, fontsize=12)\n oa_ax.legend(loc=\"upper right\", ncols=2, markerscale=markerscale, fontsize=12)\n\n # Turn off axis ticks\n bio_ax.set_xticks([])\n oa_ax.set_xticks([])\n bio_ax.set_yticks([])\n oa_ax.set_yticks([])\n\n final_spec.tight_layout(figure=final_fig, h_pad=5, w_pad=0.1)\n final_fig.savefig(os.path.join(output, \"full_image.png\"))\n final_fig.savefig(os.path.join(output, \"full_image.pdf\"))\n\n\ndef _get_args():\n from argparse import ArgumentParser\n\n parser = ArgumentParser()\n parser.add_argument(\n \"--val_root\", type=str, default=\"/local/scratch/cv_datasets/inat21/raw/val\"\n )\n parser.add_argument(\n \"--results_output\", type=str, default=\"/local/scratch/carlyn.1/test_output\"\n )\n parser.add_argument(\n \"--openai_tsne_data\",\n type=str,\n default=\"openai_tsne_rerun_top_6_remove_outliers\",\n )\n parser.add_argument(\n \"--bioclip_tsne_data\",\n type=str,\n default=\"bioclip_tsne_rerun_top_6_remove_outliers\",\n )\n parser.add_argument(\"--top_k\", type=int, default=6)\n parser.add_argument(\"--dpi\", type=float, default=500)\n\n return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n args = _get_args()\n hierarchy_label_map = _get_hierarchy_lbl_map(args.val_root)\n\n openai_features = []\n openai_labels = []\n bioclip_features = []\n bioclip_labels = []\n\n openai_tsne_dir = os.path.join(args.results_output, args.openai_tsne_data)\n bioclip_tsne_dir = os.path.join(args.results_output, args.bioclip_tsne_data)\n\n for i in range(7):\n openai_features.append(\n np.load(os.path.join(openai_tsne_dir, f\"precomputed_{i}.npy\"))\n )\n openai_labels.append(\n np.load(os.path.join(openai_tsne_dir, f\"precomputed_{i}_labels.npy\"))\n )\n bioclip_features.append(\n np.load(os.path.join(bioclip_tsne_dir, f\"precomputed_{i}.npy\"))\n )\n bioclip_labels.append(\n np.load(os.path.join(bioclip_tsne_dir, f\"precomputed_{i}_labels.npy\"))\n )\n\n output_name = os.path.join(args.results_output, \"final\")\n os.makedirs(output_name, exist_ok=True)\n\n for lvl in range(len(openai_labels)):\n for a_lbl, b_lbl in zip(openai_labels[lvl], bioclip_labels[lvl]):\n for i in range(7):\n assert a_lbl[i] == b_lbl[i], \"Labels do not match\"\n\n create_final_image(\n openai_features,\n openai_labels,\n bioclip_features,\n bioclip_labels,\n output=output_name,\n top_k=args.top_k,\n dpi=args.dpi,\n )\n"
},
{
"path": "src/evaluation/create_zoom_figure.py",
"content": "import os\nfrom argparse import ArgumentParser\n\nimport matplotlib.gridspec as gridspec\nimport matplotlib.patches as patches\nimport matplotlib.pyplot as plt\nimport matplotlib.transforms as transforms\nimport numpy as np\nfrom PIL import Image\nfrom sklearn.metrics import confusion_matrix, pairwise_distances\nfrom torchvision import transforms as T\n\nfrom evaluation.hierarchy_tree_image import get_colors\nfrom evaluation.utils import load_json\n\n\ndef _get_arrow_end(img_ax, arrow_end_code):\n xmin = img_ax.bbox.xmin\n xmax = img_ax.bbox.xmax\n ymin = img_ax.bbox.ymin\n ymax = img_ax.bbox.ymax\n\n if arrow_end_code == \"cr\":\n return xmax, (ymin + ymax) / 2\n elif arrow_end_code == \"br\":\n return xmax, ymin\n elif arrow_end_code == \"bc\":\n return (xmin + xmax) / 2, ymin\n elif arrow_end_code == \"bl\":\n return xmin, ymin\n elif arrow_end_code == \"cl\":\n return xmin, (ymin + ymax) / 2\n else:\n assert False, f\"{arrow_end_code} is not a valid arrow end code\"\n\n\ndef _load_img(path):\n resize_fn = T.Resize((256, 256), Image.BILINEAR)\n im = Image.open(path)\n im = resize_fn(im)\n im = np.array(im).astype(np.float32) / 255\n\n return im\n\n\ndef _get_rep_imgs(point, tsne_feautres, img_paths, num_imgs=4):\n x, y = point\n in_point = np.array(point)[np.newaxis, :]\n dist = pairwise_distances(tsne_feautres, in_point)[:, 0]\n idx = np.argsort(dist)\n\n return np.array(img_paths)[idx[:num_imgs]].tolist()\n\n\ndef create_zoom_figure(\n tsne_feautres,\n high_level_labels,\n img_paths,\n zoom_points,\n rep_spec_idx,\n arrow_end_codes,\n output_path,\n output_name,\n lvl,\n dpi=500,\n top_k=6,\n num_imgs=9,\n):\n \"\"\"\n tsne_feautres: 2-dim coordinates from TSNE\n high_level_labels: labels for the next tier. If we have Orders of insecta, then we should have the labels be those orders\n img_paths: The associated image paths\n zoom_points: points in the plot to show zoomed images\n rep_spec_idx: 1-to-1 with zoom_points. Where to place zoom rep images\n arrow_end_codes: where the end of the arrow should go\n output_path: where to save the figure\n output_name: what to name the figure\n lvl: the hierarchy level being visualized\n dpi: dots-per-inch of image\n top_k: top categories to visualize\n num_imgs: # of images to zoom into\n \"\"\"\n\n base_colors = get_colors()\n\n fig_width = 15\n fig_height = 7\n final_fig = plt.figure(figsize=(fig_width, fig_height), dpi=dpi)\n final_fig.set_tight_layout(True)\n final_spec = gridspec.GridSpec(3, 6)\n\n lbl_lengths = []\n sorted_lbls = sorted(list(set(high_level_labels[:, lvl])))\n for lbl in sorted_lbls:\n idx = high_level_labels[:, lvl] == lbl\n lbl_lengths.append([lbl, len(tsne_feautres[idx])])\n lbl_lengths = sorted(lbl_lengths, key=lambda x: x[1], reverse=True)\n\n plot_ax = final_fig.add_subplot(final_spec[1:, 1:5])\n\n c = 0\n for lbl in sorted_lbls:\n if top_k > 0:\n if lbl not in np.array(lbl_lengths)[:top_k, 0]:\n continue\n idx = high_level_labels[:, lvl] == lbl\n feat = tsne_feautres[idx]\n name = f\"{lbl.split('_')[-1]}\"\n\n markersize = 2\n\n # have to rasterize for .pdf to load quicker\n plot_ax.scatter(\n feat[:, 0],\n feat[:, 1],\n label=name,\n color=base_colors[c],\n alpha=0.50,\n s=markersize,\n rasterized=True,\n )\n c += 1\n\n plot_ax.legend(\n bbox_to_anchor=(0.5, 0.0),\n loc=\"upper center\",\n ncols=6,\n markerscale=8,\n fontsize=12,\n )\n\n # TSNE plot axis\n plot_ax.set_xticks([])\n plot_ax.set_yticks([])\n plt.setp(plot_ax.spines.values(), lw=0)\n\n zoom_plot_ax = []\n for i, (x, y) in enumerate(zoom_points):\n # Add Ellipse\n fig_ax = plt.gca()\n ew = 4\n eh = ew * (fig_width / fig_height)\n elip = patches.Ellipse(\n [x, y],\n height=eh,\n width=ew,\n linewidth=2,\n fill=False,\n color=\"black\",\n transform=plot_ax.transData,\n )\n plot_ax.add_patch(elip)\n\n # Get Representative images\n rep_img_paths = _get_rep_imgs(\n [x, y], tsne_feautres, img_paths, num_imgs=num_imgs\n )\n ims = [_load_img(p) for p in rep_img_paths]\n rep_img = np.concatenate(\n (\n np.concatenate(ims[:3], axis=1),\n np.concatenate(ims[3:6], axis=1),\n np.concatenate(ims[6:], axis=1),\n ),\n axis=0,\n )\n\n # Plot Representative images\n sx, sy = rep_spec_idx[i]\n img_ax = final_fig.add_subplot(final_spec[sx, sy])\n img_ax.imshow(rep_img)\n img_ax.set_xticks([])\n img_ax.set_yticks([])\n plt.setp(img_ax.spines.values(), lw=0)\n\n zoom_plot_ax.append(img_ax)\n\n final_fig.tight_layout()\n final_spec.tight_layout(figure=final_fig)\n\n # Plot arrows after final image\n for i, (x, y) in enumerate(zoom_points):\n ax = zoom_plot_ax[i]\n rep_x, rep_y = _get_arrow_end(ax, arrow_end_codes[i])\n fig_ax.annotate(\n \"\",\n xy=(rep_x, rep_y),\n xytext=(x, y),\n xycoords=transforms.IdentityTransform(),\n textcoords=plot_ax.transData,\n arrowprops=dict(arrowstyle=\"]->\", shrinkA=20, shrinkB=10, linewidth=2),\n )\n\n final_fig.savefig(os.path.join(output_path, f\"{output_name}.png\"))\n final_fig.savefig(os.path.join(output_path, f\"{output_name}.pdf\"))\n\n \"\"\"\n plt.setp(bio_ax.spines.values(), lw=3, color='black') # Set border width\n plt.setp(oa_ax.spines.values(), lw=3, color='black') # Set border width\n\n \"\"\"\n\n\ndef _get_zoom_fig_seeds(exp_type, lvl):\n # TODO: levels 5 and 6 need to seeded properly\n if exp_type == 'bioclip':\n zoom_data = {\n 0 : {\n \"zoom_points\" : [(-56, -71), (-90, -17), (-80, 57), (-23, -40), (-25, 47), (12, 95), (23, 30), (63, 49), (27, -25), (50, -80)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 1 : {\n \"zoom_points\" : [(-57, -67), (-90, -20), (-83, 60), (-27, -36), (-20, 0), (11, 100), (23, 30), (75, 37), (82, 30), (65, 15)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 2 : {\n \"zoom_points\" : [(-32, -86), (-87, -44), (-56, -27), (-57, -19), (-63, -30), (5, 95), (31, 20), (66, 54), (30, -20), (60, -80)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 1), (0, 2), (0, 0), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'bc', 'bc', 'br', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 3 : {\n \"zoom_points\" : [(-56, -71), (-90, -40), (-38, 48), (-23, -40), (-20, 0), (12, 95), (23, 10), (63, 49), (27, -20), (50, -80)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 1), (0, 0), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'br', 'bc', 'br', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 4 : {\n \"zoom_points\" : [(-17, -67), (-70, -30), (-54, 31), (-30, -18), (-18, 53), (15, 89), (29, 10), (62, 41), (28, -19), (47, -80)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'br', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 5 : {\n \"zoom_points\" : [(-56, -71), (-90, -40), (-38, 48), (-23, -40), (-20, 0), (12, 95), (23, 10), (63, 49), (27, -20), (50, -80)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'br', 'bc', 'br', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 6 : {\n \"zoom_points\" : [(-56, -71), (-90, -40), (-38, 48), (-23, -40), (-20, 0), (12, 95), (23, 10), (63, 49), (27, -20), (50, -80)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'br', 'bc', 'br', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n }\n }\n\n else:\n zoom_data = {\n 0 : {\n \"zoom_points\" : [(-28, -25), (-84, 12), (-70, 37), (-37, 31), (-7, 70), (10, 79), (25, 0), (60, 60), (64, -5), (50, -70)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 1 : {\n \"zoom_points\" : [(-53, -32), (-84, 12), (-70, 37), (-33, 31), (9, 87), (22, 82), (28, 72), (52, 58), (64, -5), (50, -70)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 2 : {\n \"zoom_points\" : [(-52, -32), (-84, 12), (-65, 37), (-30, 71), (-17, 90), (-6, 86), (9, 48), (56, 30), (11, 12), (20, -70)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (1, 5), (0, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'cl', 'bl', 'cl']\n },\n 3 : {\n \"zoom_points\" : [(-58, -25), (-64, 12), (-41, 37), (-25, 34), (-19, 19), (10, 65), (25, 15), (60, 34), (55, -10), (20, -70)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 4 : {\n \"zoom_points\" : [(-44, -37), (-59, 3), (-52, 27), (-38, 38), (-28, 13), (-5, 72), (8, 19), (57, 32), (54, -11), (19, -52)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 5 : {\n \"zoom_points\" : [(-44, -37), (-59, 3), (-52, 27), (-38, 38), (-28, 13), (-5, 72), (8, 19), (57, 32), (54, -11), (19, -52)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n },\n 6 : {\n \"zoom_points\" : [(-44, -37), (-59, 3), (-52, 27), (-38, 38), (-28, 13), (-5, 72), (8, 19), (57, 32), (54, -11), (19, -52)],\n \"rep_spec_idx\" : [(2, 0), (1, 0), (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (1, 5), (2, 5)],\n \"arrow_end_codes\" : ['cr', 'cr', 'br', 'bc', 'bc', 'bc', 'bl', 'bl', 'cl', 'cl']\n }\n }\n\n return zoom_data[lvl][\"zoom_points\"], zoom_data[lvl][\"rep_spec_idx\"], zoom_data[lvl][\"arrow_end_codes\"]\n\n\ndef get_args():\n parser = ArgumentParser()\n parser.add_argument(\n \"--exp_type\", type=str, default=\"bioclip\", choices=[\"bioclip\", \"openai\"]\n )\n parser.add_argument(\"--lvl\", type=int, default=3)\n parser.add_argument(\"--dpi\", type=int, default=300)\n parser.add_argument(\"--output_dir\", type=str, default=\"tmp/\")\n\n return parser.parse_args()\n\n\ndef _get_lvl_cls(path, lvl, is_img_path=False):\n if lvl == 0:\n return \"Root\"\n if is_img_path:\n lvls = path.split(os.path.sep)[-2]\n else:\n lvls = path\n return lvls.split(\"_\")[lvl]\n\n\ndef _calc_acc(pred_list_path, lvl, lvl_cls):\n filtered = []\n correct = 0\n total = 0\n with open(pred_list_path) as f:\n lines = f.readlines()\n for line in lines[1:]:\n path, pred_y, tgt_y, pred_cls, tgt_cls = line.split(\",\")\n if pred_y == tgt_y:\n correct += 1\n total += 1\n tgt_lvl_cls = _get_lvl_cls(tgt_cls, lvl)\n if tgt_lvl_cls == lvl_cls:\n filtered.append([path, pred_y, tgt_y, pred_cls, tgt_cls])\n\n print(f\"Zero shot accuracy on iNat21: {round(correct / total, 4) * 100}%\")\n filtered = np.array(filtered)\n matrix = confusion_matrix(filtered[:, 4], filtered[:, 3])\n print(matrix)\n identity = np.identity(n=matrix.shape[0])\n max_idx_x, max_idx_y = np.unravel_index(np.argmax(matrix * identity), matrix.shape)\n print(matrix[max_idx_x, max_idx_y])\n exit()\n\n\nif __name__ == \"__main__\":\n args = get_args()\n\n output_path = args.output_dir\n output_name = f\"intrinsic_zoom_{args.exp_type}_lvl_{args.lvl}\"\n\n if args.exp_type == \"bioclip\":\n base_path = \"tmp/11_2023_tsne_rerun_top_6_remove_outliers\"\n pred_list_path = (\n \"logs/tol1m-random-best-val-few-shot/inat21-predictions-bioclip.csv\"\n )\n\n else:\n base_path = \"tmp/openai_pretrain_tsne_rerun_top_6_remove_outliers\"\n pred_list_path = (\n \"logs/tol1m-random-best-val-few-shot/inat21-predictions-openai.csv\"\n )\n\n zoom_points, rep_spec_idx, arrow_end_codes = _get_zoom_fig_seeds(\n args.exp_type, args.lvl\n )\n\n tsne_feautres = np.load(os.path.join(base_path, f\"precomputed_{args.lvl}.npy\"))\n high_level_labels = np.load(\n os.path.join(base_path, f\"precomputed_{args.lvl}_labels.npy\")\n )\n img_paths = load_json(os.path.join(base_path, f\"precomputed_{args.lvl}_paths.json\"))\n\n accuracies = _calc_acc(\n pred_list_path, args.lvl, _get_lvl_cls(img_paths[0], args.lvl, is_img_path=True)\n )\n\n create_zoom_figure(\n tsne_feautres,\n high_level_labels,\n img_paths,\n zoom_points,\n rep_spec_idx,\n arrow_end_codes,\n output_path,\n output_name,\n args.lvl,\n dpi=args.dpi,\n )\n"
},
{
"path": "src/evaluation/data.py",
"content": "import os\nimport random\nimport json\nimport pandas as pd\nfrom PIL import Image\nfrom torchvision import datasets\nfrom torch.utils.data import Dataset, DataLoader\nfrom ..imageomics import naming_eval\n\n\ndef make_splits(directory) -> dict[str, list[str]]:\n classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())\n if not classes:\n raise FileNotFoundError(f\"Couldn't find any class folder in {directory}.\")\n\n random.seed(1337)\n shuffled = random.sample(classes, k=len(classes))\n\n # Check that they're the same order on all machines\n # err = \"not reproducible\"\n # assert (\n # shuffled[0]\n # == \"08737_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_hastata\"\n # ), err\n # assert (\n # shuffled[8000]\n # == \"03011_Animalia_Chordata_Amphibia_Anura_Microhylidae_Kaloula_pulchra\"\n # ), err\n # assert (\n # shuffled[8999]\n # == \"02422_Animalia_Arthropoda_Insecta_Odonata_Gomphidae_Arigomphus_furcifer\"\n # ), err\n # assert (\n # shuffled[9000]\n # == \"01963_Animalia_Arthropoda_Insecta_Lepidoptera_Nymphalidae_Pyronia_bathseba\"\n # ), err\n # assert (\n # shuffled[9999]\n # == \"06333_Plantae_Tracheophyta_Liliopsida_Poales_Poaceae_Avena_fatua\"\n # ), err\n\n return {\n \"pretraining\": shuffled[:],\n \"seen\": shuffled[:],\n \"unseen\": shuffled[:],\n }\n\n\nclass PretrainingData(datasets.ImageFolder):\n def find_classes(self, directory: str) -> tuple[list[str], dict[str, int]]:\n \"\"\"\n Only chooses classes that are unseen during pretraining\n \"\"\"\n splits = make_splits(directory)\n classes = splits[\"pretraining\"]\n\n class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}\n return classes, class_to_idx\n\n\nclass UnseenData(datasets.ImageFolder):\n def find_classes(self, directory: str) -> tuple[list[str], dict[str, int]]:\n \"\"\"\n Only chooses classes that are unseen during pretraining\n \"\"\"\n splits = make_splits(directory)\n classes = splits[\"unseen\"]\n\n class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}\n return classes, class_to_idx\n\n\nclass SeenData(datasets.ImageFolder):\n def find_classes(self, directory: str) -> tuple[list[str], dict[str, int]]:\n \"\"\"\n Only chooses classes that are seen during pretraining\n \"\"\"\n splits = make_splits(directory)\n classes = splits[\"seen\"]\n\n class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}\n return classes, class_to_idx\n\ndef img_loader(filepath):\n img = Image.open(filepath)\n if img.mode in (\"RGBA\", \"P\"):\n img = img.convert(\"RGB\")\n return img\n\nclass DatasetFromFile(Dataset):\n def __init__(self, filepath, label_filepath=None, transform=None, classes='asis'):\n super(DatasetFromFile, self).__init__()\n self.basefilepath = filepath\n if label_filepath is None:\n label_filepath = os.path.join(self.basefilepath, 'metadata.csv')\n else:\n label_filepath = os.path.join(self.basefilepath, label_filepath)\n\n self.data = pd.read_csv(label_filepath, index_col=0).fillna('')\n self.transform = transform\n self.data['class'] = naming_eval.to_classes(self.data,classes)\n self.classes = self.data['class'].unique()\n # create class_to_idx dict\n if 'class_idx' in self.data.columns:\n self.class_to_idx = dict([(x, y) for x, y in zip(self.data['class'], self.data['class_idx'])])\n else:\n self.class_to_idx = dict(zip(self.classes,range(len(self.classes))))\n self.data['class_idx'] = self.data['class'].apply(lambda x: self.class_to_idx[x])\n\n self.idx_to_class = dict([(v,k)for k,v in self.class_to_idx.items()])\n self.samples = self.data['filepath'].values.tolist()\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx):\n item = self.data.iloc[idx]\n filepath = os.path.join(self.basefilepath,item['filepath'])\n img = img_loader(filepath)\n if self.transform is not None:\n img = self.transform(img)\n \n target = item['class_idx']\n return img, target\n"
},
{
"path": "src/evaluation/extract_features.py",
"content": "\"\"\"\nExtract features fron iNat21 dataset for visualization.\n\"\"\"\nimport datetime\nimport logging\nimport os\nimport sys\nfrom argparse import ArgumentParser\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torchvision.datasets import ImageFolder\nfrom tqdm import tqdm\n\nfrom imageomics.naming import iNat21NameLookup\nfrom open_clip import create_model_and_transforms, trace_model\nfrom training.logger import setup_logging\n\nfrom .params import parse_args\nfrom .utils import init_device, random_seed, save_json\n\n\nclass ImageFolderWithPaths(ImageFolder):\n def __getitem__(self, index):\n img, label = super().__getitem__(index)\n path = self.imgs[index][0]\n return (img, label, path)\n\n\ndef extract_image_features(model, dloader, device, class_embeddings=None):\n feats = None\n lbls = None\n all_paths = []\n for img, lbl, paths in tqdm(dloader, desc=\"Extracting Features\"):\n img = img.to(device)\n lbl = lbl.cpu().detach().numpy()\n f = model.encode_image(img)\n f = F.normalize(f, dim=-1)\n if class_embeddings is not None:\n f = 100.0 * f @ class_embeddings\n f = f.cpu().detach().numpy()\n if feats is None:\n feats = f\n lbls = lbl\n else:\n feats = np.concatenate((feats, f), axis=0)\n lbls = np.concatenate((lbls, lbl), axis=0)\n all_paths = all_paths + list(paths)\n\n return feats, lbls, all_paths\n\n\ndef _load_model(args):\n if torch.cuda.is_available():\n # This enables tf32 on Ampere GPUs which is only 8% slower than\n # float16 and almost as accurate as float32\n # This was a default in pytorch until 1.12\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False\n\n device = init_device(args)\n\n # get the name of the experiments\n if args.name is None:\n # sanitize model name for filesystem/uri use\n model_name_safe = args.model.replace(\"/\", \"-\")\n date_str = datetime.datetime.now().strftime(\"%Y_%m_%d-%H_%M_%S\")\n args.name = \"-\".join(\n [\n date_str,\n f\"model_{model_name_safe}\",\n f\"b_{args.batch_size}\",\n f\"j_{args.workers}\",\n f\"p_{args.precision}\",\n \"zero_shot_iid\",\n ]\n )\n\n log_base_path = os.path.join(args.logs, args.name)\n args.log_path = None\n os.makedirs(log_base_path, exist_ok=True)\n log_filename = f\"out-{args.rank}\" if args.log_local else \"out.log\"\n args.log_path = os.path.join(log_base_path, log_filename)\n\n # Setup text logger\n args.log_level = logging.DEBUG if args.debug else logging.INFO\n setup_logging(args.log_path, args.log_level)\n\n # Setup checkpoint logging\n args.checkpoint_path = os.path.join(log_base_path, \"checkpoints\")\n os.makedirs(args.checkpoint_path, exist_ok=True)\n\n if (\n isinstance(args.force_image_size, (tuple, list))\n and len(args.force_image_size) == 1\n ):\n # arg is nargs, single (square) image size list -> int\n args.force_image_size = args.force_image_size[0]\n\n random_seed(args.seed, 0)\n model, _, preprocess_val = create_model_and_transforms(\n args.model,\n args.pretrained, # This is the pretrained weights\n precision=args.precision,\n device=device,\n jit=args.torchscript,\n force_quick_gelu=args.force_quick_gelu,\n force_custom_text=args.force_custom_text,\n force_patch_dropout=None,\n force_image_size=args.force_image_size,\n pretrained_image=args.pretrained_image,\n image_mean=args.image_mean,\n image_std=args.image_std,\n aug_cfg=args.aug_cfg,\n output_dict=True,\n )\n\n return model, device, preprocess_val\n\n\nif __name__ == \"__main__\":\n args = parse_args(sys.argv[1:])\n parser = ArgumentParser()\n parser.add_argument(\"--exp_type\", type=str, default=\"openai\")\n parser.add_argument(\n \"--val_root\", type=str, default=\"/local/scratch/cv_datasets/inat21/raw/val\"\n )\n parser.add_argument(\n \"--feature_output\",\n type=str,\n default=\"/local/scratch/carlyn.1/clip_paper_bio/features\",\n )\n # Be sure to set the --pretrained [path to weights]\n\n args, _ = parser.parse_known_args(namespace=args)\n\n model, device, preprocess_val = _load_model(args)\n\n random_seed(args.seed, args.rank)\n\n if args.trace:\n model = trace_model(model, batch_size=args.batch_size, device=device)\n\n data_save_path = os.path.join(args.feature_output, args.exp_type + \"_features.npy\")\n lbl_save_path = os.path.join(args.feature_output, args.exp_type + \"_labels.npy\")\n path_save_path = os.path.join(args.feature_output, args.exp_type + \"_paths.json\")\n\n model.eval()\n\n # initialize datasets\n dset = ImageFolderWithPaths(args.val_root, transform=preprocess_val)\n classes = dset.classes\n inat_name_lookup = iNat21NameLookup(inat21_root=args.val_root)\n classnames = [inat_name_lookup.taxonomic(cls) for cls in classes]\n dloader = torch.utils.data.DataLoader(\n dset,\n batch_size=args.batch_size,\n num_workers=8,\n sampler=None,\n )\n\n # logging\n args.save_logs = args.logs and args.logs.lower() != \"none\"\n\n with torch.no_grad():\n features, lbls, all_paths = extract_image_features(\n model, dloader, device, class_embeddings=None\n )\n\n np.save(data_save_path, features)\n np.save(lbl_save_path, lbls)\n save_json(path_save_path, all_paths)\n"
},
{
"path": "src/evaluation/few_shot.py",
"content": "\"\"\"\nDo few-shot classification.\n\nSingle-process. If you want to run all evaluations of a single model at once, look in scripts/.\n\"\"\"\n\nimport datetime\nimport logging\nimport os\nimport sys\n\nimport torch\nimport torch.nn.functional as F\nfrom tqdm import tqdm\nimport time\nimport json\nimport pickle\nimport numpy as np\nimport random\nimport shutil\nfrom numpy import linalg as LA\nfrom scipy.stats import mode\n\nfrom .data import DatasetFromFile\nfrom .params import parse_args\nfrom .utils import init_device, random_seed\n\nfrom ..open_clip import (\n create_model_and_transforms,\n get_cast_dtype,\n get_tokenizer,\n trace_model,\n)\nfrom ..training.logger import setup_logging\nfrom ..training.precision import get_autocast\n\n\n\ndef save_pickle(base_path, data):\n print('base_path:',base_path)\n os.makedirs(base_path, exist_ok=True)\n file = os.path.join(base_path,'pickle.p')\n print('pickle file location:',file)\n with open(file, 'wb') as f:\n pickle.dump(data, f)\n return file\n\n\ndef load_pickle(file):\n with open(file, 'rb') as f:\n return pickle.load(f)\n\n\ndef get_dataloader(dataset, batch_size, num_workers):\n return torch.utils.data.DataLoader(\n dataset,\n batch_size=batch_size,\n num_workers=num_workers,\n sampler=None,\n )\n\ndef accuracy(output, target, topk=(1,)):\n pred = output.topk(max(topk), 1, True, True)[1].t() #[batch_size, classes] -> [batch_size, 1] -> [1, batch_size], which class # topk = (values, indices)\n correct = pred.eq(target.view(1, -1).expand_as(pred)) #shape: correct=targe.view=pred=[1, batch_size], True or False\n\n return [\n float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy())\n for k in topk\n ]\n\n\ndef run(model, dataloader, args):\n autocast = get_autocast(args.precision)\n cast_dtype = get_cast_dtype(args.precision)\n with torch.no_grad():\n feature_list = []\n target_list = []\n for images, target in tqdm(dataloader, unit_scale=args.batch_size):\n target_list.append(target.numpy())\n images = images.to(args.device) #images.shape: torch.Size([batch_size, 3 rgb channels, image_height, image_width])\n if cast_dtype is not None:\n images = images.to(dtype=cast_dtype)\n target = target.to(args.device)\n \n\n with autocast():\n image_features = model.encode_image(images) #batch_size x emb_size\n image_features = F.normalize(image_features, dim=-1)\n feature_list.append(image_features.detach().cpu().numpy())\n \n file = save_pickle(log_base_path,[np.vstack(feature_list), np.hstack(target_list), dataloader.dataset.samples,dataloader.dataset.class_to_idx])\n\n return file\n\n\n\ndef few_shot_eval(model, data, args):\n results = {}\n\n logging.info(\"Starting few-shot.\")\n\n for split in data:\n logging.info(\"Building few-shot %s classifier.\", split)\n \n file = run(model, data[split], args)\n \n logging.info(\"Finished few-shot %s with total %d classes.\", split, len(data[split].dataset.classes))\n\n logging.info(\"Finished few-shot.\")\n\n return results, file\n\ndef split(select, kshot, nfold, i2c, filepath=None): \n \n test = [] #N\n target = [] #N\n train = [] #kshot x class\n label = []\n test_sample = []\n random.seed(nfold)\n for k,v in select.items():\n num_v = len(v['feature'])\n if num_v < kshot:\n logging.info(f'{i2c[k]} has only {num_v} images. Less than {kshot} images for few-shot. ')\n elif num_v < kshot+5:\n logging.info(f'{i2c[k]} has only {num_v} images. Not enough for evaluation.')\n \n random.shuffle(v['feature'])\n train.append(v['feature'][:kshot])\n test+=v['feature'][kshot:]\n test_sample+=v['sample'][kshot:]\n label+=[k for i in range(kshot)]\n test_num = num_v-kshot\n target+=[k for i in range(test_num)]\n \n flatten_train = np.vstack(train)\n label = np.array(label)\n assert kshot*len(train) == flatten_train.shape[0] == label.shape[0]\n assert len(train) == n_class\n assert len(test) == len(target) == len(test_sample)\n\n return flatten_train, label, test, target\n\ndef CL2N(x_flatten, x_mean):\n x_flatten = x_flatten - x_mean #(class, emb) = (class, emb) - (emb,)\n x_flatten = x_flatten / LA.norm(x_flatten, 2, 1)[:, None] #(class, emb) = (class, emb) / (class,1)\n return x_flatten\n\ndef get_acc(flatten_train, label, test, target, n_class, kshot, nfold):\n train_mean = flatten_train.mean(axis=0)\n \n flatten_train = CL2N(flatten_train,train_mean)\n test = CL2N(test,train_mean)\n train_center = flatten_train.reshape(n_class, kshot, flatten_train.shape[-1]).mean(1)\n\n num_NN = 1\n label = label[::kshot] #num of class\n subtract = train_center[:, None, :] - test\n distance = LA.norm(subtract, 2, axis=-1) #(train num:test num)\n idx = np.argpartition(distance, num_NN, axis=0)[:num_NN] #(num_NN:train num)\n nearest_samples = np.take(label, idx) #(num_NN:train num)\n out = mode(nearest_samples, axis=0, keepdims=True)[0]\n out = out.astype(int)\n test_label = np.array(target)\n acc = (out == test_label).mean()\n\n return acc\n\nif __name__ == \"__main__\":\n global args\n args = parse_args(sys.argv[1:])\n random_seed(args.seed, 0)\n\n if torch.cuda.is_available():\n # This enables tf32 on Ampere GPUs which is only 8% slower than\n # float16 and almost as accurate as float32\n # This was a default in pytorch until 1.12\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.benchmark = True\n\n device = init_device(args)\n\n args.save_logs = args.logs and args.logs.lower() != \"none\"\n\n # get the name of the experiments\n if args.save_logs and args.name is None:\n # sanitize model name for filesystem/uri use\n model_name_safe = args.model.replace(\"/\", \"-\")\n date_str = datetime.datetime.now().strftime(\"%Y_%m_%d-%H_%M_%S\")\n args.name = \"-\".join(\n [\n date_str,\n f\"model_{model_name_safe}\",\n f\"b_{args.batch_size}\",\n f\"p_{args.precision}\",\n \"few_shot\",\n ]\n )\n \n if args.save_logs is None:\n args.log_path = None\n else:\n log_base_path = os.path.join(args.logs, args.name)\n os.makedirs(log_base_path, exist_ok=True)\n log_filename = \"out.log\"\n args.log_path = os.path.join(log_base_path, log_filename)\n\n # Setup text logger\n args.log_level = logging.DEBUG if args.debug else logging.INFO\n setup_logging(args.log_path, args.log_level)\n\n if (\n isinstance(args.force_image_size, (tuple, list))\n and len(args.force_image_size) == 1\n ):\n # arg is nargs, single (square) image size list -> int\n args.force_image_size = args.force_image_size[0]\n\n logging.info(\"Params:\")\n if args.save_logs is None:\n for name in sorted(vars(args)):\n val = getattr(args, name)\n logging.info(f\" {name}: {val}\")\n else:\n params_file = os.path.join(args.logs, args.name, \"params.txt\")\n with open(params_file, \"w\") as f:\n for name in sorted(vars(args)):\n val = getattr(args, name)\n logging.info(f\" {name}: {val}\")\n f.write(f\"{name}: {val}\\n\")\n\n if args.task_type == 'eval':\n feature_file = args.pretrained \n else:\n model, preprocess_train, preprocess_val = create_model_and_transforms(\n args.model,\n args.pretrained,\n precision=args.precision,\n device=device,\n jit=args.torchscript,\n force_quick_gelu=args.force_quick_gelu,\n force_custom_text=args.force_custom_text,\n force_patch_dropout=None,\n force_image_size=args.force_image_size,\n pretrained_image=args.pretrained_image,\n image_mean=args.image_mean,\n image_std=args.image_std,\n aug_cfg=args.aug_cfg,\n output_dict=True,\n )\n\n if args.trace:\n model = trace_model(model, batch_size=args.batch_size, device=device)\n\n logging.info(\"Model:\")\n logging.info(f\"{str(model)}\")\n\n # initialize datasets\n data = {\n \"val-unseen\": get_dataloader(\n DatasetFromFile(args.data_root, args.label_filename, transform=preprocess_val),\n batch_size=args.batch_size,num_workers=args.workers\n ),\n }\n\n start_time = time.monotonic()\n \n model.eval()\n _, feature_file = few_shot_eval(model, data, args) \n\n end_time = time.monotonic()\n logging.info(f\"feature extraction takes: {datetime.timedelta(seconds=end_time - start_time)}\")\n\n if args.task_type == 'eval' or args.task_type == 'all':\n feature, target, samples, c2i = load_pickle(feature_file)\n\n i2c = dict([(v,k) for k,v in c2i.items()])\n\n if args.debug:\n for i in range(len(target)):\n assert target[i] == samples[i][1]\n\n\n select = dict()\n for idx in range(len(feature)):\n f = feature[idx]\n s = samples[idx]\n cat = target[idx]\n if cat in select:\n select[cat]['feature'].append(f)\n select[cat]['sample'].append(s)\n else:\n select[cat] = dict()\n select[cat]['feature'] = [f]\n select[cat]['sample'] = [s]\n \n count = sum([len(v['feature']) for v in select.values()])\n n_class = len(select)\n\n logging.info(\"Num of classes: %d.\\nNum of samples: %d.\", n_class, count)\n\n for kshot in args.kshot_list:\n \n acc_list = []\n for n in range(args.nfold):\n #split\n flatten_train, label, test, target = split(select, kshot, n, i2c)\n acc = get_acc(flatten_train, label, test, target, n_class, kshot, n)\n acc_list.append(acc)\n logging.info(f\"{kshot} shot No.{n} ACC: {acc:.4f}\")\n logging.info(\"!!!!!!Result: \")\n logging.info(\"Dataset: %s\", args.data_root)\n logging.info(\"Model: %s\", args.pretrained)\n logging.info(f\"{kshot} shot AVG ACC: {np.mean(acc_list)*100:.2f}\")\n logging.info(f\"{kshot} shot STD: {np.std(acc_list)*100:.4f}\")\n \n logging.info(f\"Done!\")\n"
},
{
"path": "src/evaluation/hierarchy_tree_image.py",
"content": "import os\nfrom datetime import timedelta\nfrom timeit import default_timer as timer\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import chi2\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\nfrom torchvision.datasets.folder import find_classes\n\nfrom evaluation.utils import load_json, save_json\n\nTAXONOMICAL_RANKS = [\n \"Kingdoms\",\n \"Phyla\",\n \"Classes\",\n \"Orders\",\n \"Families\",\n \"Genera\",\n \"Species\",\n]\n\nCOLORS = [\n (127, 60, 141),\n (17, 165, 121),\n (57, 105, 172),\n (242, 183, 1),\n (231, 63, 116),\n (128, 186, 90),\n (230, 131, 16),\n (0, 134, 149),\n (207, 28, 144),\n (249, 123, 114),\n (165, 170, 153),\n]\n\n\ndef get_colors():\n colors = []\n for c in COLORS:\n colors.append((c[0] / 255, c[1] / 255, c[2] / 255))\n return colors\n\n\ndef mahalanobis(x):\n mu = x.mean(0)\n cov = np.cov(x.T)\n\n x_mu = x - mu\n inv_covmat = np.linalg.inv(cov)\n left = np.dot(x_mu, inv_covmat)\n mahal = np.dot(left, x_mu.T)\n return mahal.diagonal()\n\n\ndef create_hierarchical_tree_vis(\n features,\n hierarchy_labels,\n img_paths,\n reduction_method=\"tsne\",\n hierarchy_label_map=None,\n top_k=6,\n output=\"\",\n rerun_reduction=False,\n verbose=False,\n remove_outliers=False,\n precomputed_reductions=None,\n dpi=100,\n):\n assert reduction_method in [\"tsne\", \"pca\"], f\"{reduction_method} not supported.\"\n os.makedirs(output, exist_ok=True)\n\n def log(x):\n if verbose:\n print(x)\n\n lvls = len(hierarchy_labels[0])\n base_colors = get_colors()\n\n def run_reduction(in_feats):\n log(\n f\"Running {reduction_method} on features: {in_feats.shape}. This may take some time.\"\n )\n start = timer()\n out_feats = reduce.fit_transform(in_feats)\n end = timer()\n log(f\"{reduction_method} completed: {timedelta(seconds=end-start)}\")\n\n return out_feats\n\n if reduction_method == \"tsne\":\n reduce = TSNE(n_jobs=16, n_components=2, verbose=2)\n elif reduction_method == \"pca\":\n reduce = PCA(2)\n\n if not rerun_reduction:\n if precomputed_reductions is None:\n features = run_reduction(features)\n else:\n features = precomputed_reductions\n saved_features = features\n saved_labels = hierarchy_labels\n saved_paths = img_paths\n else:\n saved_features = []\n saved_labels = []\n saved_paths = []\n\n data_queue = [(features, hierarchy_labels, img_paths, 0)]\n largest_name = None\n graph_data = []\n\n nrows = 3\n ncols = 3\n height = 7\n width = 5\n final_fig, axs = plt.subplots(\n nrows=nrows, ncols=ncols, figsize=(height * nrows, width * ncols), dpi=dpi\n )\n while len(data_queue) > 0:\n past_largest_name = largest_name\n cur_plot_lvl = past_largest_name if past_largest_name is not None else \"Kingdom\"\n\n feats, lbls, paths, lvl = data_queue.pop(0)\n if rerun_reduction:\n if precomputed_reductions is None:\n reduced_feats = run_reduction(feats)\n else:\n reduced_feats = precomputed_reductions[lvl]\n saved_features.append(reduced_feats)\n saved_labels.append(lbls)\n saved_paths.append(paths)\n else:\n reduced_feats = feats\n if hierarchy_label_map is not None:\n lvl_lbl_map = hierarchy_label_map[lvl]\n\n log(f\"Plotting Level {cur_plot_lvl}\")\n\n lbl_lengths = []\n sorted_lbls = sorted(list(set(lbls[:, lvl])))\n for lbl in sorted_lbls:\n idx = lbls[:, lvl] == lbl\n lbl_lengths.append([lbl, len(reduced_feats[idx])])\n lbl_lengths = sorted(lbl_lengths, key=lambda x: x[1], reverse=True)\n\n row = lvl // ncols\n col = lvl % ncols\n if lvl == 6:\n col += 1\n ax = axs[row, col]\n plt.setp(ax.spines.values(), lw=3, color=\"black\") # Set border width\n title_parts = cur_plot_lvl.split(\"_\")\n fig_title = TAXONOMICAL_RANKS[lvl]\n if lvl > 0:\n fig_title += f\" of {title_parts[-1]}\"\n ax.set_title(f\"({lvl+1}) {fig_title}\", fontsize=25, y=1.02)\n\n most_feats = None\n most_lbls = None\n highest_num = 0\n c = 0\n for lbl in sorted_lbls:\n if top_k > 0:\n if lbl not in np.array(lbl_lengths)[:top_k, 0]:\n continue\n idx = lbls[:, lvl] == lbl\n feat = reduced_feats[idx]\n if hierarchy_label_map is not None:\n name = lvl_lbl_map[str(lbl)]\n else:\n name = f\"{lbl.split('_')[-1]}\"\n\n if len(feat) > highest_num:\n highest_num = len(feat)\n if rerun_reduction:\n most_feats = feats[idx]\n else:\n most_feats = feat\n most_lbls = lbls[idx]\n most_paths = np.array(paths)[idx].tolist()\n largest_name = lbl\n if hierarchy_label_map is not None:\n largest_name = lvl_lbl_map[lbl]\n\n plot_feat = feat\n if remove_outliers:\n mah_dist = mahalanobis(feat)\n # https://www.statology.org/mahalanobis-distance-python/\n # Typically a p-value that is < .001 is considered an outlier\n p = 1 - chi2.cdf(mah_dist, feat.shape[1] - 1)\n filtered_idx = p >= 0.001\n plot_feat = feat[filtered_idx]\n\n markersize = 2\n if lvl == 5:\n markersize *= 2\n elif lvl == 6:\n markersize *= 4\n ax.scatter(\n plot_feat[:, 0],\n plot_feat[:, 1],\n label=name,\n color=base_colors[c],\n alpha=0.50,\n s=markersize,\n rasterized=True,\n ) # have to rasterize for .pdf to load quicker\n\n c += 1\n\n # Add to queue\n if (lvl + 1) < lvls:\n data_queue.append((most_feats, most_lbls, most_paths, lvl + 1))\n\n if lvl > 0:\n save_path = os.path.join(output, f\"depth_{lvl}_{past_largest_name}.png\")\n else:\n save_path = os.path.join(output, f\"depth_{lvl}.png\")\n\n markerscale = 8\n if lvl == 5:\n markerscale = 6\n elif lvl == 6:\n markerscale = 4\n ax.legend(loc=\"upper right\", ncols=2, markerscale=markerscale, fontsize=12)\n # Turn off axis ticks\n ax.set_xticks([])\n ax.set_yticks([])\n\n ax_extent = ax.get_window_extent().transformed(\n final_fig.dpi_scale_trans.inverted()\n )\n final_fig.savefig(save_path, bbox_inches=ax_extent)\n graph_data.append((save_path, lvl))\n\n for r in range(nrows):\n for c in range(ncols):\n axs[r][c].set_xticks([])\n axs[r][c].set_yticks([])\n plt.setp(axs[-1][-1].spines.values(), lw=0)\n plt.setp(axs[-1][-3].spines.values(), lw=0)\n\n final_fig.tight_layout(h_pad=1, w_pad=1)\n final_fig.savefig(os.path.join(output, \"full_image.png\"))\n final_fig.savefig(os.path.join(output, \"full_image.pdf\"))\n\n return saved_features, saved_labels, saved_paths\n\n\ndef _get_hierarchy_lbl_map(root):\n classes, class_to_idx = find_classes(root)\n idx_to_class = {val: key for (key, val) in class_to_idx.items()}\n\n return idx_to_class\n\n\ndef _get_args():\n from argparse import ArgumentParser\n\n parser = ArgumentParser()\n parser.add_argument(\n \"--val_root\", type=str, default=\"/local/scratch/cv_datasets/inat21/raw/val\"\n )\n parser.add_argument(\n \"--features_output\",\n type=str,\n default=\"/local/scratch/carlyn.1/clip_paper_bio/features\",\n )\n parser.add_argument(\"--results_output\", type=str, default=\"tmp\")\n parser.add_argument(\"--exp_type\", type=str, default=\"8_25_2023_83_epochs\")\n parser.add_argument(\n \"--reduction\", type=str, default=\"tsne\", choices=[\"tsne\", \"pca\"]\n )\n parser.add_argument(\"--rerun\", action=\"store_true\", default=False)\n parser.add_argument(\"--remove_outliers\", action=\"store_true\", default=False)\n parser.add_argument(\"--subset\", type=int, default=-1)\n parser.add_argument(\"--top_k\", type=int, default=6)\n parser.add_argument(\"--dpi\", type=float, default=500)\n\n return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n args = _get_args()\n\n hierarchy_label_map = _get_hierarchy_lbl_map(args.val_root)\n\n features = np.load(\n os.path.join(args.features_output, args.exp_type + \"_features.npy\")\n )\n labels = np.load(os.path.join(args.features_output, args.exp_type + \"_labels.npy\"))\n img_paths = load_json(\n os.path.join(args.features_output, args.exp_type + \"_paths.json\")\n )\n\n hierarchy_labels = []\n for lbl in labels:\n lvls = hierarchy_label_map[lbl].split(\"_\")[1:]\n hierarchy_labels.append(\n [\"_\".join(lvls[: lvl + 1]) for lvl in range((len(lvls)))]\n )\n\n hierarchy_labels = np.array(hierarchy_labels)\n\n folder_name = (\n f\"{args.exp_type}_{args.reduction}_{'rerun' if args.rerun else 'no_rerun'}\"\n )\n folder_name += f\"_top_{args.top_k}_{'remove_outliers' if args.remove_outliers else 'outliers_remain'}\"\n\n output_name = os.path.join(args.results_output, folder_name)\n\n if args.subset > 0:\n features = features[: args.subset]\n hierarchy_labels = hierarchy_labels[: args.subset]\n img_paths = img_paths[: args.subset]\n\n precomputed_features = None\n precompute_name = \"precomputed_0.npy\" if args.rerun else \"precomputed.npy\"\n precomputed_lbl_name = (\n \"precomputed_0_labels.npy\" if args.rerun else \"precomputed_labels.npy\"\n )\n precomputed_path_name = (\n \"precomputed_0_paths.json\" if args.rerun else \"precomputed_paths.json\"\n )\n precomputed_path = os.path.join(output_name, precompute_name)\n precomputed_label_path = os.path.join(output_name, precomputed_lbl_name)\n precomputed_path_path = os.path.join(output_name, precomputed_path_name)\n precomputed_path_path = os.path.join(output_name, precomputed_path_name)\n if os.path.exists(precomputed_path):\n if not args.rerun:\n precomputed_features = np.load(precomputed_path)\n else:\n precomputed_features = []\n for i in range(7):\n precomputed_features.append(\n np.load(os.path.join(output_name, f\"precomputed_{i}.npy\"))\n )\n\n sf, sl, sp = create_hierarchical_tree_vis(\n features,\n hierarchy_labels,\n img_paths,\n reduction_method=args.reduction,\n output=output_name,\n rerun_reduction=args.rerun,\n verbose=True,\n top_k=args.top_k,\n remove_outliers=args.remove_outliers,\n precomputed_reductions=precomputed_features,\n dpi=args.dpi,\n )\n\n if not args.rerun:\n np.save(precomputed_path, sf)\n np.save(precomputed_label_path, sl)\n np.save(precomputed_path_path, sp)\n else:\n for i, f in enumerate(sf):\n np.save(os.path.join(output_name, f\"precomputed_{i}.npy\"), f)\n for i, l in enumerate(sl):\n np.save(os.path.join(output_name, f\"precomputed_{i}_labels.npy\"), l)\n for i, p in enumerate(sp):\n save_json(os.path.join(output_name, f\"precomputed_{i}_paths.json\"), p)\n"
},
{
"path": "src/evaluation/params.py",
"content": "import argparse\nimport ast\nimport os\n\nclass ParseKwargs(argparse.Action):\n def __call__(self, parser, namespace, values, option_string=None):\n kw = {}\n for value in values:\n key, value = value.split(\"=\")\n try:\n kw[key] = ast.literal_eval(value)\n except ValueError:\n # fallback to string (avoid need to escape on command line)\n kw[key] = str(value)\n setattr(namespace, self.dest, kw)\n\n\ndef parse_args(args):\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--label_filename\",\n type=str,\n default=None,\n help=\"File path of the CSV annotation file under the base folder in --data_root.\",\n )\n parser.add_argument(\n \"--text_type\",\n type=str,\n default='asis',\n help=\"Text type of annotations for test examples.\",\n )\n parser.add_argument(\n \"--task_type\",\n type=str,\n choices=[\"retrieve\", \"eval\", \"all\"],\n default='all',\n help=\"retrieve: retrieve image embeddings from image encoder; eval: evaluate with pickle file containing image embeddings; all: evaluate from scratch which both retrieve image embeddings and evaluate the embeddings in one run.\",\n )\n parser.add_argument(\n \"--nfold\",\n type=int,\n default=5,\n help=\"The number of times of sampling training examples during few-shot.\",\n )\n parser.add_argument(\n \"--kshot_list\",\n type=int,\n nargs=\"+\",\n default=[1,5],\n help=\"A list of integers for k in k-shot.\",\n )\n parser.add_argument(\n \"--data_root\", \n type=str, \n default='', \n help=\"File path of base folder which contains images and a CSV annotation file. \"\n )\n parser.add_argument(\n \"--logs\",\n type=str,\n default=None,\n help=\"Where to store logs. Use None or 'none' to avoid storing logs.\",\n )\n parser.add_argument(\n \"--name\",\n type=str,\n default=None,\n help=\"Optional identifier for the experiment when storing logs. Otherwise use current time.\",\n )\n parser.add_argument(\n \"--workers\", type=int, default=1, help=\"Number of dataloader workers per GPU.\"\n )\n parser.add_argument(\n \"--batch-size\", type=int, default=64, help=\"Batch size per GPU.\"\n )\n parser.add_argument(\n \"--precision\",\n choices=[\"amp\", \"amp_bf16\", \"amp_bfloat16\", \"bf16\", \"fp32\"],\n default=\"amp\",\n help=\"Floating point precision.\",\n )\n parser.add_argument(\n \"--model\",\n type=str,\n default=\"ViT-B-16\",\n help=\"Name of the vision backbone to use.\",\n )\n parser.add_argument(\n \"--pretrained\",\n default=\"openai\",\n type=str,\n help=\"Use a pretrained CLIP model weights with the specified tag or file path. If running few-shot.py with --task_type=eval, use this parameter as the pickle file path.\",\n )\n parser.add_argument(\n \"--pretrained-image\",\n default=False,\n action=\"store_true\",\n help=\"Load imagenet pretrained weights for image tower backbone if available.\",\n )\n parser.add_argument(\n \"--image-mean\",\n type=float,\n nargs=\"+\",\n default=None,\n metavar=\"MEAN\",\n help=\"Override default image mean value of dataset\",\n )\n parser.add_argument(\n \"--image-std\",\n type=float,\n nargs=\"+\",\n default=None,\n metavar=\"STD\",\n help=\"Override default image std deviation of of dataset\",\n )\n parser.add_argument(\"--aug-cfg\", nargs=\"*\", default={}, action=ParseKwargs)\n parser.add_argument(\n \"--force-image-size\",\n type=int,\n nargs=\"+\",\n default=None,\n help=\"Override default image size\",\n )\n parser.add_argument(\n \"--force-quick-gelu\",\n default=False,\n action=\"store_true\",\n help=\"Force use of QuickGELU activation for non-OpenAI transformer models.\",\n )\n parser.add_argument(\n \"--force-custom-text\",\n default=False,\n action=\"store_true\",\n help=\"Force use of CustomTextCLIP model (separate text-tower).\",\n )\n parser.add_argument(\n \"--torchscript\",\n default=False,\n action=\"store_true\",\n help=\"torch.jit.script the model, also uses jit version of OpenAI models if pretrained=='openai'\",\n )\n parser.add_argument(\n \"--trace\",\n default=False,\n action=\"store_true\",\n help=\"torch.jit.trace the model for inference / eval only\",\n )\n parser.add_argument(\n \"--debug\",\n default=False,\n action=\"store_true\",\n help=\"If true, more information is logged.\",\n )\n parser.add_argument(\n \"--no-set-device-rank\",\n default=False,\n action=\"store_true\",\n help=\"Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc).\",\n )\n parser.add_argument(\n \"--seed\", \n type=int, \n default=0, \n help=\"Default random seed.\"\n )\n args, _ = parser.parse_known_args(args)\n\n return args\n"
},
{
"path": "src/evaluation/utils.py",
"content": "import json\nimport random\nimport os\nimport numpy as np\nimport torch\n\n\ndef save_json(path, obj):\n with open(path, \"w\") as f:\n json.dump(obj, f)\n\n\ndef load_json(filepath):\n if os.path.exists(filepath):\n with open(filepath, 'r') as f:\n return json.load(f)\n else:\n return None\n\n\ndef random_seed(seed=42, rank=0):\n torch.manual_seed(seed + rank)\n np.random.seed(seed + rank)\n random.seed(seed + rank)\n os.environ['PYTHONHASHSEED'] = str(seed)\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n\n\ndef init_device(args):\n args.distributed = False\n args.world_size = 1\n args.rank = 0 # global rank\n args.local_rank = 0\n\n if torch.cuda.is_available():\n if args.distributed and not args.no_set_device_rank:\n device = \"cuda:%d\" % args.local_rank\n else:\n device = \"cuda:0\"\n torch.cuda.set_device(device)\n else:\n device = \"cpu\"\n\n args.device = device\n device = torch.device(device)\n return device\n"
},
{
"path": "src/evaluation/zero_shot_iid.py",
"content": "\"\"\"\nDo zero-shot classification on IID data with both seen and unseen classes.\n\nSingle-process. If you want to run all evaluations of a single model at once, look\nin scripts/.\n\nWrites the output to a plaintext and JSON format in the logs directory.\n\"\"\"\n\nimport datetime\nimport logging\nimport os\nimport sys\nimport json\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom tqdm import tqdm\n\nfrom ..open_clip import (\n create_model_and_transforms,\n get_cast_dtype,\n get_tokenizer,\n trace_model,\n)\nfrom ..training.imagenet_zeroshot_data import openai_imagenet_template\nfrom ..training.logger import setup_logging\nfrom ..training.precision import get_autocast\n\nfrom .data import DatasetFromFile\nfrom .params import parse_args\nfrom .utils import init_device, random_seed, load_json\n\n\n\ndef get_dataloader(dataset, batch_size, num_workers):\n return torch.utils.data.DataLoader(\n dataset,\n batch_size=batch_size,\n num_workers=num_workers,\n sampler=None,\n )\n\n\ndef zero_shot_classifier(model, classnames, templates, args):\n tokenizer = get_tokenizer(args.model)\n with torch.no_grad():\n zeroshot_weights = []\n for classname in tqdm(classnames):\n texts = [template(classname) for template in templates] # format with class\n texts = tokenizer(texts).to(args.device) # tokenize\n class_embeddings = model.encode_text(texts)\n class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0)\n class_embedding /= class_embedding.norm()\n zeroshot_weights.append(class_embedding)\n zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(args.device)\n return zeroshot_weights\n\n\ndef 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 dict([\n (k,float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy()))\n for k in topk\n ])\n\n\ndef run(model, classifier, dataloader, args):\n autocast = get_autocast(args.precision)\n cast_dtype = get_cast_dtype(args.precision)\n with torch.no_grad():\n n = 0.0\n topk = dict()\n for i in (1,min(len(dataloader.dataset.classes),3), min(len(dataloader.dataset.classes),5)):\n topk[i] = 0.0\n for images, target in tqdm(dataloader, unit_scale=args.batch_size):\n images = images.to(args.device) #images.shape: torch.Size([batch_size, 3 rgb channels, image_height, image_width])\n if cast_dtype is not None:\n images = images.to(dtype=cast_dtype)\n target = target.to(args.device)\n\n with autocast():\n # predict\n image_features = model.encode_image(images)\n image_features = F.normalize(image_features, dim=-1)\n # logits = 100.0 * image_features @ classifier\n logits = model.logit_scale.exp() * image_features @ classifier\n\n # measure accuracy\n acc = accuracy(logits, target, topk=topk.keys())\n for k,v in acc.items():\n topk[k] += v\n n += images.size(0)\n\n for k,v in acc.items():\n topk[k] /= n\n return topk\n\n\ndef zero_shot_eval(model, data, args):\n results = {}\n\n logging.info(\"Starting zero-shot.\")\n\n for split in data:\n logging.info(\"Building zero-shot %s classifier.\", split)\n classnames = [c for c in data[split].dataset.classes]\n\n classifier = zero_shot_classifier(\n model, classnames, openai_imagenet_template, args\n )\n\n topk = run(model, classifier, data[split], args)\n\n for k,v in topk.items():\n results[f\"{split}-top{k}\"] = v\n\n logging.info(\"Finished zero-shot %s with total %d classes.\", split, len(data[split].dataset.classes))\n\n logging.info(\"Finished zero-shot.\")\n\n return results\n\n\nif __name__ == \"__main__\":\n args = parse_args(sys.argv[1:])\n\n if torch.cuda.is_available():\n # This enables tf32 on Ampere GPUs which is only 8% slower than\n # float16 and almost as accurate as float32\n # This was a default in pytorch until 1.12\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False\n\n device = init_device(args)\n\n args.save_logs = args.logs and args.logs.lower() != \"none\"\n\n # get the name of the experiments\n if args.save_logs and args.name is None:\n # sanitize model name for filesystem/uri use\n model_name_safe = args.model.replace(\"/\", \"-\")\n date_str = datetime.datetime.now().strftime(\"%Y_%m_%d-%H_%M_%S\")\n args.name = \"-\".join(\n [\n date_str,\n f\"model_{model_name_safe}\",\n f\"b_{args.batch_size}\",\n f\"j_{args.workers}\",\n f\"p_{args.precision}\",\n \"zero_shot_iid\",\n ]\n )\n if args.save_logs is None:\n args.log_path = None\n else:\n log_base_path = os.path.join(args.logs, args.name)\n args.log_path = None\n os.makedirs(log_base_path, exist_ok=True)\n log_filename = \"out.log\"\n args.log_path = os.path.join(log_base_path, log_filename)\n\n # Setup text logger\n args.log_level = logging.DEBUG if args.debug else logging.INFO\n setup_logging(args.log_path, args.log_level)\n\n if (\n isinstance(args.force_image_size, (tuple, list))\n and len(args.force_image_size) == 1\n ):\n # arg is nargs, single (square) image size list -> int\n args.force_image_size = args.force_image_size[0]\n\n random_seed(args.seed, 0)\n model, preprocess_train, preprocess_val = create_model_and_transforms(\n args.model,\n args.pretrained,\n precision=args.precision,\n device=device,\n jit=args.torchscript,\n force_quick_gelu=args.force_quick_gelu,\n force_custom_text=args.force_custom_text,\n force_patch_dropout=None,\n force_image_size=args.force_image_size,\n pretrained_image=args.pretrained_image,\n image_mean=args.image_mean,\n image_std=args.image_std,\n aug_cfg=args.aug_cfg,\n output_dict=True,\n )\n\n random_seed(args.seed, args.rank)\n\n if args.trace:\n model = trace_model(model, batch_size=args.batch_size, device=device)\n\n logging.info(\"Model:\")\n logging.info(f\"{str(model)}\")\n logging.info(\"Params:\")\n if args.save_logs is None:\n for name in sorted(vars(args)):\n val = getattr(args, name)\n logging.info(f\" {name}: {val}\")\n else:\n params_file = os.path.join(args.logs, args.name, \"params.txt\")\n with open(params_file, \"w\") as f:\n for name in sorted(vars(args)):\n val = getattr(args, name)\n logging.info(f\" {name}: {val}\")\n f.write(f\"{name}: {val}\\n\")\n\n # initialize datasets\n data = {\n \"val-unseen\": get_dataloader(\n DatasetFromFile(args.data_root, args.label_filename, transform=preprocess_val, classes=args.text_type),\n batch_size=args.batch_size,num_workers=args.workers\n ),\n }\n\n model.eval()\n metrics = zero_shot_eval(model, data, args)\n \n logging.info(\"Results:\")\n for key, value in metrics.items():\n logging.info(f\" {key}: {value*100:.2f}\")\n logging.info(\"Done.\")\n"
},
{
"path": "src/imageomics/__init__.py",
"content": ""
},
{
"path": "src/imageomics/disk.py",
"content": "\"\"\"\nAll filepaths.\n\"\"\"\n\neol_vernacularnames_csv = \"data/eol/vernacularnames.csv\"\neol_scraped_page_ids_csv = \"data/eol/scraped_page_ids.csv\"\neol_taxon_tab = \"data/eol/dh21/taxon.tab\"\n\nitis_hierarchy_csv = (\n \"/fs/ess/PAS2136/open_clip/data/itis/data/interim/species_level_taxonomy_chains.csv\"\n)\n\ninaturalist_vernacularnames_csv = \"data/inat/dwca/VernacularNames-english.csv\"\ninaturalist_taxa_csv = \"data/inat/dwca/taxa.csv\"\n\nbioscan_metadata_jsonld = (\n \"/fs/scratch/PAS2136/bioscan/BIOSCAN_Insect_Dataset_metadata.jsonld\"\n)\n\nresolved_jsonl = \"/fs/ess/PAS2136/open_clip/data/resolved.jsonl\"\n\n# Actual datasets\neol_root_dir = \"/fs/ess/PAS2136/eol/data/interim/media_cargo_archive\"\ninat21_root_dir = \"/fs/ess/PAS2136/foundation_model/inat21/raw/train\"\nbioscan_root_dir = \"/fs/scratch/PAS2136/bioscan/cropped_256\"\n\n# rare species\nseen_in_training_json = \"data/rarespecies/seen_in_training.json\"\nunseen_in_training_json = \"data/rarespecies/unseen_in_training.json\"\n\n# Files we make\n\neol_name_lookup_json = \"data/names/eol_name_lookup.json\"\ninat21_name_lookup_json = \"data/names/inat21_name_lookup.json\"\nbioscan_name_lookup_json = \"data/names/bioscan_name_lookup.json\"\n\ncommon_name_lookup_json = \"data/names/scientific_to_common.json\"\n"
},
{
"path": "src/imageomics/disk_reproduce.py",
"content": "\"\"\"\nAll filepaths.\n\"\"\"\n\n# Actual datasets\nDATASET_DIR = \"data/TreeOfLife-10M/dataset/\"\n\neol_root_dir = f\"{DATASET_DIR}eol\"\ninat21_root_dir = f\"{DATASET_DIR}inat21/train\"\nbioscan_root_dir = f\"{DATASET_DIR}bioscan\"\n\n# rare species\nseen_in_training_json = \"data/rarespecies/seen_in_training.json\"\nunseen_in_training_json = \"data/rarespecies/unseen_in_training.json\"\n\n# Files we make\nMETADATA_DIR = \"data/TreeOfLife-10M/metadata/\"\n\neol_name_lookup_json = f\"{METADATA_DIR}naming/eol_name_lookup.json\"\ninat21_name_lookup_json = f\"{METADATA_DIR}naming/inat21_name_lookup.json\"\nbioscan_name_lookup_json = f\"{METADATA_DIR}naming/bioscan_name_lookup.json\"\n\ndb = f\"{METADATA_DIR}mapping.sqlite\"\n"
},
{
"path": "src/imageomics/eol.py",
"content": "import dataclasses\nimport logging\nimport re\n\nfrom . import disk, helpers, naming\n\nlogger = logging.getLogger()\n\neol_filename_pattern = re.compile(r\"(\\d+)_(\\d+)_eol.*jpg\")\n\n\n@dataclasses.dataclass(frozen=True)\nclass ImageFilename:\n \"\"\"\n Represents a filename like 12784812_51655800_eol-full-size-copy.jpg\n \"\"\"\n\n content_id: int\n page_id: int\n ext: str\n raw: str\n\n @classmethod\n def from_filename(cls, filename):\n match = eol_filename_pattern.match(filename)\n if not match:\n raise ValueError(filename)\n return cls(int(match.group(1)), int(match.group(2)), \"jpg\", filename)\n\n\nclass EolNameLookup:\n def __init__(self):\n # dict[page id, (rank, name, parent id)]\n # These are known errors that we preemptively correct.\n self._lookup = {\n 331434: (\"species\", \"latouchii\", 42333),\n 52338888: (\"species\", \"lateralis\", 42333),\n }\n\n self._resourcepk_to_pageid = {} # dict[resource pk, page id]\n\n for row in helpers.csvreader(disk.eol_taxon_tab, delimiter=\"\\t\"):\n if \"eolID\" in row and row[\"eolID\"]:\n page_id = int(row[\"eolID\"])\n else:\n continue\n self._resourcepk_to_pageid[row[\"taxonID\"]] = page_id\n\n # Takes about 21 seconds\n for row in helpers.csvreader(disk.eol_taxon_tab, delimiter=\"\\t\"):\n if \"eolID\" in row and row[\"eolID\"]:\n page_id = int(row[\"eolID\"])\n else:\n continue\n\n rank = row.get(\"taxonRank\", \"\").lower()\n\n if rank == \"class\":\n rank = \"cls\"\n\n parent_resource_pk = row.get(\"parentNameUsageID\", \"\")\n parent_page_id = self._resourcepk_to_pageid.get(parent_resource_pk)\n name = row[\"canonicalName\"]\n\n if page_id in self._lookup:\n logger.info(\n \"Skipping page id because it's present. [page id: %s]\", page_id\n )\n continue\n\n if (\n rank == \"species\"\n and name.capitalize() == name\n and len(name.split()) == 2\n ):\n # We can likely assume that the first word is the genus.\n genus, species = name.split()\n self._lookup[page_id] = (\"species\", species.lower(), parent_page_id)\n else:\n self._lookup[page_id] = (rank, name.lower(), parent_page_id)\n\n def guess_value(self, name, genus_lookup):\n if len(name.split()) == 2:\n genus, species = name.split()\n genus_pageid = genus_lookup.get(genus)\n return (\"species\", species, genus_pageid)\n\n return (\"species\", name, None)\n\n def add_low_quality_names(self, page_ids_with_imgs):\n \"\"\"\n Low quality names often do not include the entire taxonomic chain. So we only add these names for the page ids that actually matter, which are the ones with images.\n \"\"\"\n # Sometimes we can find a species' genus by searching by name.\n # It doesn't always work well because species names can be reused for different genera\n genus_lookup = {\n name: page_id\n for page_id, (rank, name, _) in self._lookup.items()\n if rank == \"genus\"\n }\n n_genus = sum(1 for (rank, _, _) in self._lookup.values() if rank == \"genus\")\n n_unique_genus = len(genus_lookup)\n logger.info(\n \"Only %d/%d genus are unique (%.1f)%%\",\n n_unique_genus,\n n_genus,\n n_unique_genus / n_genus * 100,\n )\n\n for row in helpers.csvreader(disk.eol_scraped_page_ids_csv):\n page_id = int(row[\"page_id\"])\n if page_id in self._lookup or page_id not in page_ids_with_imgs:\n continue\n\n name = naming.clean_name(row[\"scientific_name\"])\n if name in genus_lookup:\n logger.debug(\n \"Name '%s' (https://eol.org/pages/%d) is a genus (https://eol.org/pages/%d).\",\n name,\n page_id,\n genus_lookup.get(name),\n )\n\n value = self.guess_value(name, genus_lookup)\n if value:\n self._lookup[page_id] = value\n\n def taxon(self, page_id):\n taxon = {rank: \"\" for rank in naming.taxon_ranks}\n while page_id in self._lookup:\n rank, value, parent_id = self._lookup[page_id]\n if rank in naming.taxon_ranks:\n taxon[rank] = value\n page_id = parent_id\n\n return naming.Taxon(**taxon)\n"
},
{
"path": "src/imageomics/eol_reproduce.py",
"content": "import dataclasses\nimport logging\nimport re\n\n\nlogger = logging.getLogger()\n\n# ToL ID generated from uuid.uuid4(), uuid regex from https://stackoverflow.com/a/6640851\ntol_filename_pattern = re.compile(\"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}.*jpg$\")\n\n\n@dataclasses.dataclass(frozen=True)\nclass ImageFilename:\n \"\"\"\n Represents a filename like .jpg, returns zero for the content and page IDs\n \"\"\"\n\n tol_id: str\n ext: str\n raw: str\n\n @classmethod\n def from_filename(cls, filename):\n match = tol_filename_pattern.match(filename)\n if not match:\n raise ValueError(filename)\n return cls(filename.split(\".\")[0], \"jpg\", filename)\n"
},
{
"path": "src/imageomics/evobio10m.py",
"content": "import dataclasses\nimport sqlite3\n\n\ndef get_outdir(tag):\n return f\"/fs/ess/PAS2136/open_clip/data/evobio10m-{tag}\"\n\n\nschema = \"\"\"\nCREATE TABLE IF NOT EXISTS eol (\n content_id INT NOT NULL,\n page_id INT NOT NULL,\n evobio10m_id TEXT NOT NULL PRIMARY KEY\n);\n\nCREATE TABLE IF NOT EXISTS inat21 (\n filename TEXT NOT NULL,\n cls_name TEXT NOT NULL,\n cls_num INT NOT NULL,\n evobio10m_id TEXT NOT NULL PRIMARY KEY\n);\n\nCREATE TABLE IF NOT EXISTS bioscan (\n part INT NOT NULL,\n filename TEXT NOT NULL,\n evobio10m_id TEXT NOT NULL PRIMARY KEY\n);\n\n-- evobio10m_id is a foreign key for one of the three other tables.\nCREATE TABLE IF NOT EXISTS split (\n evobio10m_id TEXT NOT NULL PRIMARY KEY,\n is_val INTEGER NOT NULL,\n is_train_small INTEGER NOT NULL\n);\n\nPRAGMA journal_mode=WAL; -- write-ahead log\n\"\"\"\n\n\ndef get_db(path):\n try:\n db = sqlite3.connect(path, timeout=120)\n except sqlite3.OperationalError as err:\n print(f\"Could not connect to {path} ({err}).\")\n raise\n\n db.execute(\"PRAGMA busy_timeout = 120000;\") # 120 second timeout\n db.commit()\n db.executescript(schema)\n db.commit()\n return db\n\n\ndef load_splits(db_path):\n db = get_db(db_path)\n train_stmt = \"SELECT evobio10m_id FROM split WHERE is_val = 0;\"\n train_small_stmt = (\n \"SELECT evobio10m_id FROM split WHERE is_val = 0 AND is_train_small = 1;\"\n )\n val_stmt = \"SELECT evobio10m_id FROM split WHERE is_val = 1;\"\n\n splits = {\n \"train\": {row[0] for row in db.execute(train_stmt).fetchall()},\n \"val\": {row[0] for row in db.execute(val_stmt).fetchall()},\n \"train_small\": {row[0] for row in db.execute(train_small_stmt).fetchall()},\n }\n db.close()\n return splits\n\n\n@dataclasses.dataclass(frozen=True)\nclass DatasetId:\n eol_content_id: int = None\n eol_page_id: int = None\n bioscan_part: int = None\n bioscan_filename: str = None\n inat21_filename: str = None\n inat21_cls_name: str = None\n inat21_cls_num: int = None\n\n def __post_init__(self):\n assert (\n (self.eol_content_id and self.eol_page_id)\n or (self.bioscan_part and self.bioscan_filename)\n or (\n self.inat21_filename\n and self.inat21_cls_name\n and self.inat21_cls_num is not None\n )\n ), repr(self)\n\n if self.inat21_filename:\n assert self.inat21_filename.endswith(\".jpg\")\n\n if self.bioscan_filename:\n assert self.bioscan_filename.endswith(\".jpg\")\n\n def to_tuple(self):\n return (\n self.eol_content_id,\n self.eol_page_id,\n self.bioscan_part,\n self.bioscan_filename,\n self.inat21_filename,\n self.inat21_cls_name,\n self.inat21_cls_num,\n )\n"
},
{
"path": "src/imageomics/evobio10m_reproduce.py",
"content": "import dataclasses\nimport sqlite3\nimport os\n\nbase_dir = os.path.dirname(os.path.abspath(__file__))\n\ndef get_outdir(tag):\n outdir = \"data/TreeOfLife-10M/dataset/evobio10m\"\n return os.path.abspath(f\"{outdir}-{tag}\")\n # return f\"/fs/ess/PAS2136/open_clip/data/evobio10m-{tag}\"\n\n\nschema = \"\"\"\nCREATE TABLE IF NOT EXISTS eol (\n content_id INT NOT NULL,\n page_id INT NOT NULL,\n evobio10m_id TEXT NOT NULL PRIMARY KEY\n);\n\nCREATE TABLE IF NOT EXISTS inat21 (\n filename TEXT NOT NULL,\n cls_name TEXT NOT NULL,\n cls_num INT NOT NULL,\n evobio10m_id TEXT NOT NULL PRIMARY KEY\n);\n\nCREATE TABLE IF NOT EXISTS bioscan (\n part INT NOT NULL,\n filename TEXT NOT NULL,\n evobio10m_id TEXT NOT NULL PRIMARY KEY\n);\n\n-- evobio10m_id is a foreign key for one of the three other tables.\nCREATE TABLE IF NOT EXISTS split (\n evobio10m_id TEXT NOT NULL PRIMARY KEY,\n is_val INTEGER NOT NULL,\n is_train_small INTEGER NOT NULL\n);\n\nPRAGMA journal_mode=WAL; -- write-ahead log\n\"\"\"\n\n\ndef get_db(path):\n try:\n db = sqlite3.connect(path, timeout=120)\n except sqlite3.OperationalError as err:\n print(f\"Could not connect to {path} ({err}).\")\n raise\n\n db.execute(\"PRAGMA busy_timeout = 120000;\") # 120 second timeout\n db.commit()\n db.executescript(schema)\n db.commit()\n return db\n\n\ndef load_splits(db_path):\n db = get_db(db_path)\n train_stmt = \"SELECT evobio10m_id FROM split WHERE is_val = 0;\"\n train_small_stmt = (\n \"SELECT evobio10m_id FROM split WHERE is_val = 0 AND is_train_small = 1;\"\n )\n val_stmt = \"SELECT evobio10m_id FROM split WHERE is_val = 1;\"\n\n splits = {\n \"train\": {row[0] for row in db.execute(train_stmt).fetchall()},\n \"val\": {row[0] for row in db.execute(val_stmt).fetchall()},\n \"train_small\": {row[0] for row in db.execute(train_small_stmt).fetchall()},\n }\n db.close()\n return splits\n\n\n@dataclasses.dataclass(frozen=True)\nclass DatasetId:\n eol_content_id: int = None\n eol_page_id: int = None\n bioscan_part: int = None\n bioscan_filename: str = None\n inat21_filename: str = None\n inat21_cls_name: str = None\n inat21_cls_num: int = None\n\n def __post_init__(self):\n assert (\n (self.eol_content_id and self.eol_page_id)\n or (self.bioscan_part and self.bioscan_filename)\n or (\n self.inat21_filename\n and self.inat21_cls_name\n and self.inat21_cls_num is not None\n )\n ), repr(self)\n\n if self.inat21_filename:\n assert self.inat21_filename.endswith(\".jpg\")\n\n if self.bioscan_filename:\n assert self.bioscan_filename.endswith(\".jpg\")\n\n def to_tuple(self):\n return (\n self.eol_content_id,\n self.eol_page_id,\n self.bioscan_part,\n self.bioscan_filename,\n self.inat21_filename,\n self.inat21_cls_name,\n self.inat21_cls_num,\n )\n"
},
{
"path": "src/imageomics/helpers.py",
"content": "import csv\nimport logging\nimport os\nimport random\nimport sqlite3\nimport time\n\nfrom tqdm import tqdm\n\nlogger = logging.getLogger(f\"helpers(p{os.getpid()})\")\n\n\ndef csvreader(filepath, **kwargs):\n with open(filepath) as fd:\n reader = csv.DictReader(fd, **kwargs)\n yield from tqdm(reader, desc=os.path.basename(filepath))\n\n\ndef executerobustly(db, stmt, values, *, max_attempts=-1):\n attempt = 0\n while max_attempts < 0 or attempt < max_attempts:\n attempt += 1\n try:\n db.executemany(stmt, values)\n db.commit()\n logger.debug(\"Successful commit. [attempt: %d]\", attempt)\n return\n except sqlite3.OperationalError as err:\n logger.warning(\"Error. [attempt: %d, err: %s]\", attempt, err)\n\n if attempt == max_attempts:\n raise err\n\n sleep = attempt**2 + random.randrange(10)\n logger.info(\"Sleeping. [attempt: %d, sleep: %d]\", attempt, sleep)\n time.sleep(sleep)\n\n\nif __name__ == \"__main__\":\n log_format = \"[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s\"\n logging.basicConfig(level=logging.INFO, format=log_format)\n\n test_db_path = \"helpers-test.sqlite\"\n db = sqlite3.connect(test_db_path)\n executerobustly(\n db, \"INSERT INTO missing (name) VALUES (?)\", [(\"a\",), (\"b\",)], max_attempts=4\n )\n os.remove(test_db_path)\n"
},
{
"path": "src/imageomics/naming.py",
"content": "import collections\nimport dataclasses\nimport json\nimport logging\nimport os\nimport re\n\nfrom tqdm import tqdm\n\nfrom . import disk, helpers\n\nlogger = logging.getLogger()\n\ntaxon_ranks = (\"kingdom\", \"phylum\", \"cls\", \"order\", \"family\", \"genus\", \"species\")\n\n\ndef clean_rank(value):\n assert isinstance(value, str)\n # Remove HTML\n value = strip_html(value).lower()\n\n # Sometimes values are not known, but not empty.\n if value in (\"(unidentified)\", \"unknown\", \"pleasehelp\"):\n value = \"\"\n return value\n\n\n@dataclasses.dataclass\nclass Taxon:\n kingdom: str\n phylum: str\n cls: str\n order: str\n family: str\n genus: str\n species: str\n\n def __post_init__(self):\n for rank in taxon_ranks:\n value = getattr(self, rank)\n setattr(self, rank, clean_rank(value))\n\n # Remove names and years\n self.species = clean_name(self.species)\n\n # Metazoa -> Animalia, Archaeplastida -> Plantae, Chloroplastida -> Plantae\n if self.kingdom == \"metazoa\":\n self.kingdom = \"animalia\"\n if self.kingdom == \"archaeplastida\":\n self.kingdom = \"plantae\"\n if self.kingdom == \"chloroplastida\":\n self.kingdom = \"plantae\"\n\n # If species contains the genus, fix that.\n if self.species: # Check that species is not empty.\n maybe_genus, *rest = self.species.split()\n\n # See if genus is in species label\n if maybe_genus == self.genus and rest:\n # Remove the genus from the species\n self.species = \" \".join(rest)\n\n # See if genus is in species label and genus is missing\n if not self.genus and maybe_genus and rest:\n # Remove the genus from the species\n self.species = \" \".join(rest)\n # Add genus\n self.genus = maybe_genus\n\n # If species is duplicated, fix that\n if len(self.species.split()) == 2:\n first, second = self.species.split()\n if first == second:\n self.species = first\n\n for rank in taxon_ranks:\n value = getattr(self, rank)\n setattr(self, rank, clean_rank(value))\n\n @property\n def empty(self):\n return not (\n self.kingdom\n or self.phylum\n or self.cls\n or self.order\n or self.family\n or self.genus\n or self.species\n )\n\n @property\n def filled(self):\n return (\n self.kingdom\n and self.phylum\n and self.cls\n and self.order\n and self.family\n and self.genus\n and self.species\n )\n\n @property\n def taxonomic(self):\n name = \" \".join(\n [\n self.kingdom.capitalize(),\n self.phylum.capitalize(),\n self.cls.capitalize(),\n self.order.capitalize(),\n self.family.capitalize(),\n self.genus.capitalize(),\n self.species.lower(),\n ]\n ).strip()\n\n # Remove double whitespaces\n return \" \".join(name.split())\n\n @property\n def scientific(self):\n name = \" \".join(\n [\n self.genus.capitalize(),\n self.species.lower(),\n ]\n ).strip()\n\n # Remove double whitespaces and make sure the first letter is capitalized.\n return \" \".join(name.split()).capitalize()\n\n @property\n def tagged(self):\n return [\n (key, value)\n for key, value in [\n (\"kingdom\", self.kingdom.capitalize()),\n (\"phylum\", self.phylum.capitalize()),\n (\"class\", self.cls.capitalize()),\n (\"order\", self.order.capitalize()),\n (\"family\", self.family.capitalize()),\n (\"genus\", self.genus.capitalize()),\n (\"species\", self.species.lower()),\n ]\n if value\n ]\n\n def to_tuple(self):\n return (\n self.kingdom.capitalize(),\n self.phylum.capitalize(),\n self.cls.capitalize(),\n self.order.capitalize(),\n self.family.capitalize(),\n self.genus.capitalize(),\n self.species.lower(),\n )\n\n def to_dict(self):\n return {\n \"kingdom\": self.kingdom.capitalize(),\n \"phylum\": self.phylum.capitalize(),\n \"cls\": self.cls.capitalize(),\n \"order\": self.order.capitalize(),\n \"family\": self.family.capitalize(),\n \"genus\": self.genus.capitalize(),\n \"species\": self.species.lower(),\n }\n\n def better(self, other):\n \"\"\"\n Returns if self is a strictly more informative taxon than other.\n \"\"\"\n self_tags = dict(self.tagged)\n other_tags = dict(other.tagged)\n\n # Look for tags present in other that are different in self\n for tag, self_value in self_tags.items():\n other_value = other_tags.get(tag, None)\n if other_value and other_value != self_value:\n # Other name has a non-null tag that is different.\n # Can't decide which is better, self or other\n return False\n\n # Look for tags missing in other that are present in self\n for tag, other_value in other_tags.items():\n self_value = self_tags.get(tag, None)\n if not other_value and self_value:\n # We have a value that other is missing\n return True\n\n return False\n\n\nclass NameLookup:\n \"\"\"Lookup from a key to taxonomic name.\n Meant to be subclassed for each different data source.\n \"\"\"\n\n def taxon(self, key: object): # Taxon | None\n \"\"\"Returns a Taxon object\"\"\"\n raise NotImplementedError()\n\n def keys(self): # list[object]\n raise NotImplementedError()\n\n\nclass BioscanNameLookup(NameLookup):\n def __init__(self):\n self.lookup = {} # dict[str, Taxon]\n\n with open(disk.bioscan_metadata_jsonld) as fd:\n bioscan_metadata = json.load(fd)\n\n for row in tqdm(bioscan_metadata, desc=\"Loading Bioscan metadata\"):\n taxon = Taxon(\n \"Animalia\",\n \"Arthropoda\",\n \"Insecta\",\n row[\"order\"] if row[\"order\"] != \"not_classified\" else \"\",\n row[\"family\"] if row[\"family\"] != \"not_classified\" else \"\",\n row[\"genus\"] if row[\"genus\"] != \"not_classified\" else \"\",\n row[\"species\"] if row[\"species\"] != \"not_classified\" else \"\",\n )\n self.lookup[row[\"image_file\"]] = taxon\n\n def taxon(self, key):\n return self.lookup.get(key)\n\n def keys(self):\n return list(self.lookup.keys())\n\n\nclass iNat21NameLookup(NameLookup):\n def __init__(self, inat21_root=\"/fs/ess/PAS2136/foundation_model/inat21/raw/train\"):\n self._taxa = {}\n\n for clsdir in os.listdir(inat21_root):\n index, king, phyl, cls, ord, fam, genus, species = clsdir.split(\"_\")\n index = int(index, base=10)\n taxon = Taxon(king, phyl, cls, ord, fam, genus, species)\n self._taxa[index] = taxon\n\n def taxon(self, key):\n return self._taxa.get(key)\n\n def keys(self): # list[int]\n return list(self._taxa.keys())\n\n\nclass CommonNameLookup(dict):\n def __init__(\n self,\n ):\n # ITIS\n for row in helpers.csvreader(disk.itis_hierarchy_csv):\n # Create a Taxon object because it normalizes and cleans\n taxon = Taxon(\n row[\"Kingdom\"],\n row[\"Phylum\"],\n row[\"Class\"],\n row[\"Order\"],\n row[\"Family\"],\n row[\"Genus\"],\n row[\"Species\"],\n )\n\n # These are not always the same length.\n commons = row[\"terminal_vernacular\"].split(\", \")\n langs = row[\"terminal_vernacular_lang\"].split(\", \")\n\n for com, lang in zip(commons, langs):\n if com and lang == \"English\" and id not in self:\n self[taxon.scientific] = com\n break\n\n # iNaturalist\n inaturalist_id_to_taxon = {}\n inaturalist_id_to_scientific = {}\n\n for row in helpers.csvreader(disk.inaturalist_taxa_csv):\n # Create a Taxon object because it normalizes and cleans\n taxon = Taxon(\n row[\"kingdom\"],\n row[\"phylum\"],\n row[\"class\"],\n row[\"order\"],\n row[\"family\"],\n row[\"genus\"],\n row[\"specificEpithet\"],\n )\n if row[\"taxonRank\"] in (\"species\", \"variety\"):\n inaturalist_id_to_taxon[int(row[\"id\"])] = taxon\n inaturalist_id_to_scientific[int(row[\"id\"])] = row[\"scientificName\"]\n\n for row in helpers.csvreader(disk.inaturalist_vernacularnames_csv):\n name = row[\"vernacularName\"]\n if not name:\n continue\n\n taxon = inaturalist_id_to_taxon.get(int(row[\"id\"]))\n if taxon and taxon.scientific not in self:\n self[taxon.scientific] = name\n\n scientific = inaturalist_id_to_scientific.get(int(row[\"id\"]))\n if scientific and scientific not in self:\n self[scientific] = name\n\n # EOL\n # First pass, only use preferred names\n for row in helpers.csvreader(disk.eol_vernacularnames_csv):\n if row[\"language_code\"] != \"eng\": # only keep english\n continue\n\n if not bool(row[\"is_preferred_by_eol\"]):\n continue\n\n common = clean_name(row[\"vernacular_string\"])\n scientific = strip_html(row[\"canonical_form\"])\n\n if scientific and common and scientific not in self:\n self[scientific] = common\n\n # Second pass, fill in any missing values\n for row in helpers.csvreader(disk.eol_vernacularnames_csv):\n if row[\"language_code\"] != \"eng\": # only keep english\n continue\n\n common = clean_name(row[\"vernacular_string\"])\n scientific = strip_html(row[\"canonical_form\"])\n\n if scientific and common and scientific not in self:\n self[scientific] = common\n\n\nclass NameUpgrader:\n \"\"\" \"\"\"\n\n known_duplicate_values = {\n \"actinobacteria\",\n \"aquificae\",\n \"chlamydiae\",\n \"chrysiogenetes\",\n \"collembola\",\n \"deferribacteres\",\n \"diplura\",\n \"elusimicrobia\",\n \"gemmatimonadetes\",\n \"micrognathozoa\",\n \"nitrospira\",\n \"protura\",\n \"protozoa\",\n \"thermodesulfobacteria\",\n \"thermotogae\",\n (\"leptogaster\", \"flavipes\"),\n }\n\n def __init__(self):\n # List of all changes made during upgrade()\n self.changes = collections.Counter()\n\n # Known exceptions\n self._lookup = {\n \"collembola\": ((\"cls\", \"order\"), \"arthropoda\"),\n \"diplura\": ((\"cls\", \"order\"), \"arthropoda\"),\n \"protura\": ((\"cls\", \"order\"), \"arthropoda\"),\n \"actinobacteria\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"aquificae\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"chlamydiae\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"chrysiogenetes\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"deferribacteres\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"elusimicrobia\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"gemmatimonadetes\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"micrognathozoa\": ((\"phylum\", \"cls\"), \"animalia\"),\n \"nitrospira\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"protozoa\": ((\"kingdom\", \"phylum\"), None),\n \"thermodesulfobacteria\": ((\"phylum\", \"cls\"), \"bacteria\"),\n \"thermotogae\": ((\"phylum\", \"cls\"), \"bacteria\"),\n (\"leptogaster\", \"flavipes\"): (\"scientific\", \"asilidae\"),\n }\n\n assert (\n set(self._lookup.keys()) == self.known_duplicate_values\n ), \"Update either self._lookup or self.known_duplicate_values\"\n\n self._errors = collections.Counter()\n\n for row in helpers.csvreader(disk.itis_hierarchy_csv):\n taxon = Taxon(\n row[\"Kingdom\"],\n row[\"Phylum\"],\n row[\"Class\"],\n row[\"Order\"],\n row[\"Family\"],\n row[\"Genus\"],\n row[\"Species\"],\n )\n self._add_taxon(taxon)\n\n if self._errors:\n print(self._errors)\n\n # Use resolved.jsonl\n with open(disk.resolved_jsonl) as fd:\n for line in fd:\n line = json.loads(line)\n scientific = next(iter(line.keys()))\n genus, *rest = scientific.lower().split()\n species = \" \".join(rest)\n\n ranks = next(iter(line[scientific].values()))\n\n taxon = Taxon(\n ranks.get(\"kingdom\", \"\"),\n ranks.get(\"phylum\", \"\"),\n ranks.get(\"class\", \"\"),\n ranks.get(\"order\", \"\"),\n ranks.get(\"family\", \"\"),\n genus,\n species,\n )\n\n self._add_taxon(taxon, handle_existing=\"old\")\n\n if self._errors:\n breakpoint()\n\n def _add_taxon(self, taxon, handle_existing=\"raise\"):\n \"\"\"\n Adds a taxon to the lookup.\n taxon: Taxon object\n handle_existing: either 'raise', 'old', 'new', or 'log'. In the case of an existing key that is differet:\n * 'raise' will raise an error.\n * 'old' will ignore the new key and use the old key.\n * 'new' will ignore the old key and use the new key.\n * 'log' will ignore the new key and store the error in self._errors\n \"\"\"\n\n # Known errors\n if taxon.family == \"charinidae\" and taxon.order == \"squamata\":\n taxon.family = \"boidae\"\n\n for rank, value, parent in [\n (\"scientific\", (taxon.genus, taxon.species), taxon.family),\n (\"family\", taxon.family, taxon.order),\n (\"order\", taxon.order, taxon.cls),\n (\"cls\", taxon.cls, taxon.phylum),\n (\"phylum\", taxon.phylum, taxon.kingdom),\n (\"kingdom\", taxon.kingdom, None),\n ]:\n if not value:\n continue\n\n # Known typo\n if value == \"arthropda\":\n value == \"arthropoda\"\n\n if value in self._lookup:\n # Matches, so no problem.\n if self._lookup[value] == (rank, parent):\n continue\n\n # We know about this error already.\n if value in self.known_duplicate_values:\n continue\n\n if handle_existing == \"raise\":\n raise ValueError(\n f\"{self._lookup[value]} != {(rank, parent)} for {value}\"\n )\n elif handle_existing == \"old\":\n continue\n elif handle_existing == \"new\":\n pass\n elif handle_existing == \"log\":\n self._errors[(value, self._lookup[value], (rank, parent))] += 1\n continue\n else:\n raise ValueError(handle_existing)\n\n self._lookup[value] = (rank, parent)\n\n def fill(self, ranks, name):\n \"\"\"\n Fills ranks with\n ranks (dict[str, str]): initial filled in ranks. Maps from taxon_ranks to values.\n name (str): initial name to start filling in from.\n\n Returns ranks, which is also modified by this method.\n \"\"\"\n\n def set_rank(key, value):\n \"\"\"Fills in a key-value pair in ranks. If ranks[key] != value, make a note of it in self.changes.\"\"\"\n existing = ranks.get(key, \"\")\n if existing and existing != value:\n self.changes[(key, existing, value)] += 1\n\n ranks[key] = value\n\n # Recursively go up the chain to fill the name in\n while name in self._lookup:\n rank, parent_name = self._lookup[name]\n if isinstance(rank, tuple):\n for r in rank:\n set_rank(r, name)\n elif rank in taxon_ranks:\n set_rank(rank, name)\n name = parent_name\n\n return ranks\n\n def upgrade(self, taxon):\n name, rank = find_initial_name(taxon)\n\n while True:\n ranks = {rank: getattr(taxon, rank) for rank in taxon_ranks}\n ranks = self.fill(ranks, name)\n taxon = Taxon(**ranks)\n if taxon.filled:\n return taxon\n\n name, rank = find_initial_name(taxon, failed=rank)\n if (name, rank) == (None, None):\n return Taxon(**ranks)\n\n return Taxon(**ranks)\n\n\ndef find_initial_name(taxon: Taxon, failed=None) -> tuple[str, str]:\n \"\"\"\n Problem: kingdom and phylum is filled in, cls is missing.\n upgrade sees that kingdom is not empty and uses that as top rank.\n Really we want the highest rank that is continuous to species\n But then what happens with K, P, _, O, F, _, _? -> want to use O\n K, P, _, O, F, G, S -> use O\n K, P, _, O, F, _, S -> use O, can't fill in genus\n K, P, _, _, F, _, S -> use F, can't fill in genus\n K, _, C, _, F, G, S -> use F\n\n TODO: use the species and find all possible genuses, then use those to compare against families or higher ranks\n \"\"\"\n if failed == \"scientific\":\n # Can't give anything further down the tree\n return None, None\n\n # going from scientific up to kingdom\n current = ((taxon.genus, taxon.species), \"scientific\")\n for rank in reversed(taxon_ranks[:-2]):\n if rank == failed:\n break\n if getattr(taxon, rank):\n current = (getattr(taxon, rank), rank)\n else:\n return current\n\n return current\n\n\ndef strip_html(string):\n \"\"\"Just removes ... tags and similar. Not complete.\"\"\"\n\n def clean(s):\n return re.sub(r\"<.*?>(.*?)\", r\"\\1\", s)\n\n while string != clean(string):\n string = clean(string)\n\n return string\n\n\ndef clean_name(name):\n name = strip_html(name.lower()).strip()\n\n patterns = [\n r\"\\(\", # Remove anything after parens\n r\"ssp\\.\", # ssp. indicates subspecies\n r\"subsp\\.\", # subsp. indicates subspecies\n r\"var\\.\", # var. indicates variant\n r\"\\w+, \\w+ & \\w+\", # name, name & name\n r\"\\w+ & \\w+\", # name & name\n r\"\\w+ \\d\\d\\d\\d\", # name year\n r\"\\w+, \\d\\d\\d\\d\", # name, year\n r\"\\d\\d\\d\\d\", # year\n r\" [A-z]\\.\", # initial\n r\" \", # linebreak\n ]\n for pattern in patterns:\n match = re.search(pattern, name)\n if match:\n start, end = match.span()\n name = name[:start].strip()\n\n return name\n\n\ndef load_name_lookup(path, keytype=str):\n \"\"\"\n Returns dict[key, (taxonomic, common)]\n \"\"\"\n with open(path) as fd:\n return {\n keytype(key): (Taxon(*taxa), common, classes)\n for key, (taxa, common, classes) in json.load(fd).items()\n }\n\n\ndef get_common(taxon, common):\n if common:\n return common\n if taxon.scientific:\n return taxon.scientific\n\n return taxon.taxonomic\n"
},
{
"path": "src/imageomics/naming_eval.py",
"content": "import collections\nimport dataclasses\nimport json\nimport logging\nimport os\nimport re\n\nfrom tqdm import tqdm\n\nfrom . import disk, helpers\n\nlogger = logging.getLogger()\n\ndef load_json(filepath):\n with open(filepath, 'r') as f:\n return json.load(f)\n\ndef dataset_class_to_taxon(cls):\n tiers = cls.split(\"_\")\n if cls[0].isdigit():\n index, *tiers = cls.split(\"_\")\n index = int(index, base=10)\n return Taxon(*tiers, dataset_id=index)\n else:\n return Taxon(*cls.split(\"_\"))\n\n@dataclasses.dataclass\nclass Taxon:\n kingdom: str = \"\"\n phylum: str = \"\"\n cls: str = \"\"\n order: str = \"\"\n family: str = \"\"\n genus: str = \"\"\n species: str = \"\"\n subspecies: str = \"\"\n # id from inaturalist.org (taxa.csv)\n website_id: int = -1\n # id from the inat21 dataset\n dataset_id: int = -1\n # common name (from VernacularNames-english.csv)\n common_name: str = \"\"\n\n def to_tuple(self):\n return (\n self.kingdom.capitalize(),\n self.phylum.capitalize(),\n self.cls.capitalize(),\n self.order.capitalize(),\n self.family.capitalize(),\n self.genus.capitalize(),\n self.species.lower(),\n )\n\n def to_dict(self):\n return {\n \"kingdom\": self.kingdom.capitalize(),\n \"phylum\": self.phylum.capitalize(),\n \"cls\": self.cls.capitalize(),\n \"order\": self.order.capitalize(),\n \"family\": self.family.capitalize(),\n \"genus\": self.genus.capitalize(),\n \"species\": self.species.lower(),\n }\n \n @property\n def scientific_name(self):\n if not self.subspecies == '':\n return self.genus.capitalize() + ' ' + self.species.lower() + ' ' + self.subspecies.lower()\n elif not self.species == '':\n return self.genus.capitalize() + ' ' + self.species.lower()\n elif not self.genus == '':\n return self.genus.capitalize()\n else:\n return None\n \n @property\n def taxonomic_name(self):\n if not self.subspecies == '':\n if self.kingdom == \"\":\n return \" \".join([\n self.genus.capitalize(),\n self.species.lower(),\n self.subspecies.lower()\n ])\n else:\n return \" \".join([\n self.kingdom.capitalize(),\n self.phylum.capitalize(),\n self.cls.capitalize(),\n self.order.capitalize(),\n self.family.capitalize(),\n self.genus.capitalize(),\n self.species.lower(),\n self.subspecies.lower()\n ])\n else:\n if self.kingdom == \"\":\n return \" \".join([\n self.genus.capitalize(),\n self.species.lower(),\n ])\n else:\n return \" \".join([\n self.kingdom.capitalize(),\n self.phylum.capitalize(),\n self.cls.capitalize(),\n self.order.capitalize(),\n self.family.capitalize(),\n self.genus.capitalize(),\n self.species.lower(),\n ])\n @property\n def get_common_name(self):\n return self.common_name\n\n @property\n def sci_common_name(self):\n if self.common_name == '':\n return self.scientific_name\n else:\n return self.scientific_name + ' with common name ' + self.get_common_name\n \n @property\n def taxon_common_name(self):\n if self.common_name == '':\n return self.taxonomic_name\n else:\n return self.taxonomic_name + ' with common name ' + self.get_common_name\n\ndef to_classes(data,text_type):\n if text_type == 'asis':\n return data['class']\n\n data_view = data.drop(columns=list(set(data.keys()) - set(Taxon.__dict__.keys())))\n\n if text_type == 'sci':\n return data_view.apply(lambda x: Taxon(**x.to_dict()).scientific_name, axis=1).values.tolist()\n elif text_type == 'taxon':\n return data_view.apply(lambda x: Taxon(**x.to_dict()).taxonomic_name, axis=1).values.tolist()\n elif text_type == 'com':\n return data_view.apply(lambda x: Taxon(**x.to_dict()).get_common_name, axis=1).values.tolist()\n elif text_type == 'sci_com':\n return data_view.apply(lambda x: Taxon(**x.to_dict()).sci_common_name, axis=1)\n elif text_type == 'taxon_com':\n return data_view.apply(lambda x: Taxon(**x.to_dict()).taxon_common_name, axis=1)\n else:\n raise ValueError(\"text type is not acceptable.\")\n"
},
{
"path": "src/imageomics/naming_reproduce.py",
"content": "import dataclasses\nimport json\nimport logging\nimport re\n\n\nlogger = logging.getLogger()\n\ntaxon_ranks = (\"kingdom\", \"phylum\", \"cls\", \"order\", \"family\", \"genus\", \"species\")\n\ndef clean_rank(value):\n assert isinstance(value, str)\n # Remove HTML\n value = strip_html(value).lower()\n\n # Sometimes values are not known, but not empty.\n if value in (\"(unidentified)\", \"unknown\", \"pleasehelp\"):\n value = \"\"\n return value\n\n\n@dataclasses.dataclass\nclass Taxon:\n kingdom: str\n phylum: str\n cls: str\n order: str\n family: str\n genus: str\n species: str\n\n def __post_init__(self):\n for rank in taxon_ranks:\n value = getattr(self, rank)\n setattr(self, rank, clean_rank(value))\n\n # Remove names and years\n self.species = clean_name(self.species)\n\n # Metazoa -> Animalia, Archaeplastida -> Plantae, Chloroplastida -> Plantae\n if self.kingdom == \"metazoa\":\n self.kingdom = \"animalia\"\n if self.kingdom == \"archaeplastida\":\n self.kingdom = \"plantae\"\n if self.kingdom == \"chloroplastida\":\n self.kingdom = \"plantae\"\n\n # If species contains the genus, fix that.\n if self.species: # Check that species is not empty.\n maybe_genus, *rest = self.species.split()\n\n # See if genus is in species label\n if maybe_genus == self.genus and rest:\n # Remove the genus from the species\n self.species = \" \".join(rest)\n\n # See if genus is in species label and genus is missing\n if not self.genus and maybe_genus and rest:\n # Remove the genus from the species\n self.species = \" \".join(rest)\n # Add genus\n self.genus = maybe_genus\n\n # If species is duplicated, fix that\n if len(self.species.split()) == 2:\n first, second = self.species.split()\n if first == second:\n self.species = first\n\n for rank in taxon_ranks:\n value = getattr(self, rank)\n setattr(self, rank, clean_rank(value))\n\n @property\n def empty(self):\n return not (\n self.kingdom\n or self.phylum\n or self.cls\n or self.order\n or self.family\n or self.genus\n or self.species\n )\n\n @property\n def filled(self):\n return (\n self.kingdom\n and self.phylum\n and self.cls\n and self.order\n and self.family\n and self.genus\n and self.species\n )\n\n @property\n def taxonomic(self):\n name = \" \".join(\n [\n self.kingdom.capitalize(),\n self.phylum.capitalize(),\n self.cls.capitalize(),\n self.order.capitalize(),\n self.family.capitalize(),\n self.genus.capitalize(),\n self.species.lower(),\n ]\n ).strip()\n\n # Remove double whitespaces\n return \" \".join(name.split())\n\n @property\n def scientific(self):\n name = \" \".join(\n [\n self.genus.capitalize(),\n self.species.lower(),\n ]\n ).strip()\n\n # Remove double whitespaces and make sure the first letter is capitalized.\n return \" \".join(name.split()).capitalize()\n\n @property\n def tagged(self):\n return [\n (key, value)\n for key, value in [\n (\"kingdom\", self.kingdom.capitalize()),\n (\"phylum\", self.phylum.capitalize()),\n (\"class\", self.cls.capitalize()),\n (\"order\", self.order.capitalize()),\n (\"family\", self.family.capitalize()),\n (\"genus\", self.genus.capitalize()),\n (\"species\", self.species.lower()),\n ]\n if value\n ]\n\n def to_tuple(self):\n return (\n self.kingdom.capitalize(),\n self.phylum.capitalize(),\n self.cls.capitalize(),\n self.order.capitalize(),\n self.family.capitalize(),\n self.genus.capitalize(),\n self.species.lower(),\n )\n\n def to_dict(self):\n return {\n \"kingdom\": self.kingdom.capitalize(),\n \"phylum\": self.phylum.capitalize(),\n \"cls\": self.cls.capitalize(),\n \"order\": self.order.capitalize(),\n \"family\": self.family.capitalize(),\n \"genus\": self.genus.capitalize(),\n \"species\": self.species.lower(),\n }\n\n def better(self, other):\n \"\"\"\n Returns if self is a strictly more informative taxon than other.\n \"\"\"\n self_tags = dict(self.tagged)\n other_tags = dict(other.tagged)\n\n # Look for tags present in other that are different in self\n for tag, self_value in self_tags.items():\n other_value = other_tags.get(tag, None)\n if other_value and other_value != self_value:\n # Other name has a non-null tag that is different.\n # Can't decide which is better, self or other\n return False\n\n # Look for tags missing in other that are present in self\n for tag, other_value in other_tags.items():\n self_value = self_tags.get(tag, None)\n if not other_value and self_value:\n # We have a value that other is missing\n return True\n\n return False\n\n\nclass NameLookup:\n \"\"\"Lookup from a key to taxonomic name.\n Meant to be subclassed for each different data source.\n \"\"\"\n\n def taxon(self, key: object): # Taxon | None\n \"\"\"Returns a Taxon object\"\"\"\n raise NotImplementedError()\n\n def keys(self): # list[object]\n raise NotImplementedError()\n\n\ndef find_initial_name(taxon: Taxon, failed=None) -> tuple[str, str]:\n \"\"\"\n Problem: kingdom and phylum is filled in, cls is missing.\n upgrade sees that kingdom is not empty and uses that as top rank.\n Really we want the highest rank that is continuous to species\n But then what happens with K, P, _, O, F, _, _? -> want to use O\n K, P, _, O, F, G, S -> use O\n K, P, _, O, F, _, S -> use O, can't fill in genus\n K, P, _, _, F, _, S -> use F, can't fill in genus\n K, _, C, _, F, G, S -> use F\n\n TODO: use the species and find all possible genuses, then use those to compare against families or higher ranks\n \"\"\"\n if failed == \"scientific\":\n # Can't give anything further down the tree\n return None, None\n\n # going from scientific up to kingdom\n current = ((taxon.genus, taxon.species), \"scientific\")\n for rank in reversed(taxon_ranks[:-2]):\n if rank == failed:\n break\n if getattr(taxon, rank):\n current = (getattr(taxon, rank), rank)\n else:\n return current\n\n return current\n\ndef strip_html(string):\n \"\"\"Just removes ... tags and similar. Not complete.\"\"\"\n\n def clean(s):\n return re.sub(r\"<.*?>(.*?)\", r\"\\1\", s)\n\n while string != clean(string):\n string = clean(string)\n\n return string\n\ndef clean_name(name):\n name = strip_html(name.lower()).strip()\n\n patterns = [\n r\"\\(\", # Remove anything after parens\n r\"ssp\\.\", # ssp. indicates subspecies\n r\"subsp\\.\", # subsp. indicates subspecies\n r\"var\\.\", # var. indicates variant\n r\"\\w+, \\w+ & \\w+\", # name, name & name\n r\"\\w+ & \\w+\", # name & name\n r\"\\w+ \\d\\d\\d\\d\", # name year\n r\"\\w+, \\d\\d\\d\\d\", # name, year\n r\"\\d\\d\\d\\d\", # year\n r\" [A-z]\\.\", # initial\n r\" \", # linebreak\n ]\n for pattern in patterns:\n match = re.search(pattern, name)\n if match:\n start, end = match.span()\n name = name[:start].strip()\n\n return name\n\ndef load_name_lookup(path, keytype=str):\n \"\"\"\n Returns dict[key, (taxonomic, common)]\n \"\"\"\n with open(path) as fd:\n return {\n keytype(key): (Taxon(*taxa), common, classes)\n for key, (taxa, common, classes) in json.load(fd).items()\n }\n\ndef get_common(taxon, common):\n if common:\n return common\n if taxon.scientific:\n return taxon.scientific\n\n return taxon.taxonomic\n"
},
{
"path": "src/imageomics/test_naming.py",
"content": "from . import naming\n\n\ndef test_strip_html():\n raw = \"Zygodon viridissimus viridissimus\"\n expected = \"Zygodon viridissimus viridissimus\"\n assert naming.strip_html(raw) == expected\n\n\ndef test_strip_html_recursive():\n raw = \"Zygodon viridissimus viridissimus\"\n expected = \"Zygodon viridissimus viridissimus\"\n assert naming.strip_html(raw) == expected\n\n\ndef test_clean_name_good_names():\n names = [\"Gigartina papillata\", \"Uncinia hookeri\", \"Ostrea\"]\n for name in names:\n assert naming.clean_name(name) == name.lower()\n\n\ndef test_clean_name_parens():\n raw = \"Atrypanius cinerascens (Bates 1864)\"\n expected = \"atrypanius cinerascens\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_comma_in_parens():\n raw = \"Xymene huttoni (Murdoch, 1900)\"\n expected = \"xymene huttoni\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_extra_after_parens():\n raw = \"Huperzia dacrydioides (Baker) Pic.Serm.\"\n expected = \"huperzia dacrydioides\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_subspecies():\n raw = \"Lagophylla ramosissima ssp. ramosissima\"\n expected = \"lagophylla ramosissima\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_subspecies2():\n raw = \"Lagocheirus araneiformis subsp. parvulus Casey 1913\"\n expected = \"lagocheirus araneiformis\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_name_year():\n raw = \"Megacyllene asteca Chevrolat 1860\"\n expected = \"megacyllene asteca\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_name_comma_year():\n raw = \"Partula subangulata Pease, 1870\"\n expected = \"partula subangulata\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_variant():\n raw = \"Echinocereus rigidissimus var. rubrispinus\"\n expected = \"echinocereus rigidissimus\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_three_names():\n raw = \"Collotheca orchidacea Meksuwan, Pholpunthin & Segers, 2013\"\n expected = \"collotheca orchidacea\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_ampersand():\n raw = \"Agosia yarrowi Jordan & Evermann\"\n expected = \"agosia yarrowi\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_linebreak():\n raw = \"Pellaea boivinii var. boivinii\"\n expected = \"pellaea boivinii\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_clean_name_initials():\n raw = \"Stylidium spinulosum r.br.\"\n expected = \"stylidium spinulosum\"\n assert naming.clean_name(raw) == expected\n\n\ndef test_find_initial_name():\n taxon = naming.Taxon(\"k\", \"p\", \"\", \"o\", \"f\", \"g\", \"s\")\n expected = (\"o\", \"order\")\n assert naming.find_initial_name(taxon) == expected\n\n\ndef test_find_initial_name_no_genus():\n taxon = naming.Taxon(\"k\", \"p\", \"\", \"o\", \"f\", \"\", \"s\")\n expected = (\"o\", \"order\")\n assert naming.find_initial_name(taxon) == expected\n\n\ndef test_find_initial_name_no_genus2():\n taxon = naming.Taxon(\"k\", \"p\", \"\", \"\", \"f\", \"\", \"s\")\n expected = (\"f\", \"family\")\n assert naming.find_initial_name(taxon) == expected\n\n\ndef test_find_intial_name_two_holes():\n taxon = naming.Taxon(\"k\", \"\", \"c\", \"\", \"f\", \"g\", \"s\")\n expected = (\"f\", \"family\")\n assert naming.find_initial_name(taxon) == expected\n\n\ndef test_find_intial_name_failed_order():\n taxon = naming.Taxon(\"k\", \"p\", \"\", \"o\", \"f\", \"g\", \"s\")\n expected = (\"f\", \"family\")\n assert naming.find_initial_name(taxon, failed=\"order\") == expected\n\n\ndef test_find_intial_name_failed_family():\n taxon = naming.Taxon(\"k\", \"p\", \"\", \"o\", \"f\", \"g\", \"s\")\n expected = ((\"g\", \"s\"), \"scientific\")\n assert naming.find_initial_name(taxon, failed=\"family\") == expected\n\n\ndef test_find_intial_name_failed_scientific():\n taxon = naming.Taxon(\"k\", \"p\", \"\", \"o\", \"f\", \"g\", \"s\")\n expected = (None, None)\n assert naming.find_initial_name(taxon, failed=\"scientific\") == expected\n\n\ndef test_taxon_fills_genus_from_species_with_initials():\n species = \"Morinda citrifolia L.\"\n expected = naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"Morinda\", \"citrifolia\")\n assert naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"\", species) == expected\n\n\ndef test_taxon_fills_genus_from_species_with_two_initials():\n species = \"Stylidium spinulosum r.br.\"\n expected = naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"stylidium\", \"spinulosum\")\n assert naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"\", species) == expected\n\n\ndef test_taxon_fills_genus_from_species_with_biologist_name():\n species = \"Drosera aliciae r.hamet\"\n expected = naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"drosera\", \"aliciae\")\n assert naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"\", species) == expected\n\n\ndef test_taxon_removes_duplicate_species():\n species = \"geospiza fuliginosa fuliginosa\"\n expected = naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"geospiza\", \"fuliginosa\")\n assert naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"\", species) == expected\n\n\ndef test_taxon_removes_duplicate_species2():\n species = \"geospiza fuliginosa fuliginosa fuliginosa\"\n expected = naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"geospiza\", \"fuliginosa\")\n assert naming.Taxon(\"\", \"\", \"\", \"\", \"\", \"\", species) == expected\n"
},
{
"path": "src/imageomics/test_wds.py",
"content": "import multiprocessing\nimport tempfile\n\nfrom . import wds\n\n\ndef test_choose_next_shard_no_missing():\n existing = [0, 1, 2]\n init_counter = 0\n expected_seq = [3, 4]\n\n counter = multiprocessing.Value(\"I\", init_counter, lock=True)\n with tempfile.TemporaryDirectory() as tmpdir:\n for expected in expected_seq:\n writer = wds.ShardWriter(tmpdir, counter, no_init=True)\n with counter.get_lock():\n writer.choose_next_shard(existing)\n\n assert writer.shard == expected\n\n\ndef test_choose_next_shard_one_missing():\n existing = [0, 1, 2, 4, 5]\n init_counter = 0\n expected_first = 3\n expected_second = 6\n\n counter = multiprocessing.Value(\"I\", init_counter, lock=True)\n with tempfile.TemporaryDirectory() as tmpdir:\n writer = wds.ShardWriter(tmpdir, counter, no_init=True)\n with counter.get_lock():\n writer.choose_next_shard(existing)\n\n assert writer.shard == expected_first\n\n writer = wds.ShardWriter(tmpdir, counter, no_init=True)\n with counter.get_lock():\n writer.choose_next_shard(existing)\n\n assert writer.shard == expected_second\n\n\ndef test_choose_next_shard_two_missing():\n existing = [0, 1, 2, 5]\n init_counter = 0\n expected_seq = [3, 4, 6]\n\n counter = multiprocessing.Value(\"I\", init_counter, lock=True)\n with tempfile.TemporaryDirectory() as tmpdir:\n for expected in expected_seq:\n writer = wds.ShardWriter(tmpdir, counter, no_init=True)\n with counter.get_lock():\n writer.choose_next_shard(existing)\n print(expected, writer.shard, writer.next_shard.value)\n assert writer.shard == expected\n\n\ndef test_choose_next_shard_two_missing_non_sequential():\n existing = [0, 1, 2, 4, 6]\n init_counter = 0\n expected_seq = [3, 5, 7]\n\n counter = multiprocessing.Value(\"I\", init_counter, lock=True)\n with tempfile.TemporaryDirectory() as tmpdir:\n for expected in expected_seq:\n writer = wds.ShardWriter(tmpdir, counter, no_init=True)\n with counter.get_lock():\n writer.choose_next_shard(existing)\n\n assert writer.shard == expected\n"
},
{
"path": "src/imageomics/wds.py",
"content": "import logging\nimport multiprocessing\nimport os\nimport re\n\nimport webdataset\n\n\nclass ShardWriter:\n \"\"\"Like TarWriter but splits into multiple shards.\"\"\"\n\n def __init__(\n self,\n outdir: str,\n next_shard: multiprocessing.Value,\n *,\n digits: int = 6,\n maxcount: int = 100000,\n maxsize: float = 3e9,\n no_init: bool = False,\n **kw,\n ):\n \"\"\"Create a ShardWriter.\n\n :param pattern: output file pattern\n :param maxcount: maximum number of records per shard (Default value = 100000)\n :param maxsize: maximum size of each shard (Default value = 3e9)\n :param kw: other options passed to TarWriter\n \"\"\"\n self.kw = kw\n self.maxcount = maxcount\n self.maxsize = maxsize\n\n self.tarstream = None\n self.outdir = outdir\n self.pattern = f\"shard-%0{digits}d.tar\"\n self.parse_pattern = re.compile(f\"shard-(\\\\d{{{digits}}})\\\\.tar\")\n self.total = 0\n self.count = 0\n self.size = 0\n self.fname = None\n\n self.next_shard = next_shard\n self.logger = logging.getLogger(f\"shard-writer(p{os.getpid()})\")\n\n if not no_init:\n self.next_stream()\n\n def choose_next_shard(self, existing):\n if not existing:\n self.shard = 0\n self.next_shard.value = self.shard + 1\n return\n\n existing = set(existing)\n for i in range(max(existing)):\n if i < self.next_shard.value:\n continue\n\n if i in existing:\n continue\n\n self.shard = i\n self.next_shard.value = self.shard + 1\n return\n\n self.shard = max(self.next_shard.value, max(existing) + 1)\n self.next_shard.value = self.shard + 1\n\n def next_stream(self):\n \"\"\"Close the current stream and move to the next.\"\"\"\n self.finish()\n\n with self.next_shard.get_lock():\n # Get a list of existing files\n existing = []\n for file in os.listdir(self.outdir):\n match = self.parse_pattern.match(file)\n if match is None:\n continue\n num = int(match.group(1), base=10)\n existing.append(num)\n existing = sorted(existing)\n\n # Choose next shard\n self.choose_next_shard(existing)\n\n self.fname = os.path.join(self.outdir, self.pattern % self.shard)\n stream = open(self.fname, \"wb\")\n self.logger.info(\"Opened shard %s.\", self.fname)\n self.tarstream = webdataset.TarWriter(stream, **self.kw)\n self.count = 0\n self.size = 0\n\n def write(self, obj):\n \"\"\"Write a sample.\n\n :param obj: sample to be written\n \"\"\"\n if (\n self.tarstream is None\n or self.count >= self.maxcount\n or self.size >= self.maxsize\n ):\n self.next_stream()\n size = self.tarstream.write(obj)\n self.count += 1\n self.total += 1\n self.size += size\n\n def finish(self):\n \"\"\"Finish all writing (use close instead).\"\"\"\n if self.tarstream is not None:\n self.tarstream.close()\n self.logger.info(\"Closed shard %s.\", self.fname)\n assert self.fname is not None\n self.tarstream = None\n\n def close(self):\n \"\"\"Close the stream.\"\"\"\n self.finish()\n del self.tarstream\n del self.shard\n del self.count\n del self.size\n\n def __enter__(self):\n \"\"\"Enter context.\"\"\"\n return self\n\n def __exit__(self, *args, **kw):\n \"\"\"Exit context.\"\"\"\n self.close()\n"
},
{
"path": "src/open_clip/__init__.py",
"content": "from .coca_model import CoCa\nfrom .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD\nfrom .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_loss\nfrom .factory import list_models, add_model_config, get_model_config, load_checkpoint\nfrom .loss import ClipLoss, DistillClipLoss, CoCaLoss\nfrom .model import CLIP, CustomTextCLIP, CLIPTextCfg, CLIPVisionCfg, \\\n convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype\nfrom .openai import load_openai_model, list_openai_models\nfrom .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model, \\\n get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained\nfrom .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub\nfrom .tokenizer import SimpleTokenizer, tokenize, decode\nfrom .transform import image_transform, AugmentationCfg\n"
},
{
"path": "src/open_clip/coca_model.py",
"content": "from typing import Optional\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nimport numpy as np\nfrom dataclasses import dataclass\n\nfrom .transformer import (\n LayerNormFp32,\n LayerNorm,\n QuickGELU,\n MultimodalTransformer,\n)\nfrom .model import CLIPTextCfg, CLIPVisionCfg, _build_vision_tower, _build_text_tower\n\ntry:\n from transformers import (\n BeamSearchScorer,\n LogitsProcessorList,\n TopPLogitsWarper,\n TopKLogitsWarper,\n RepetitionPenaltyLogitsProcessor,\n MinLengthLogitsProcessor,\n MaxLengthCriteria,\n StoppingCriteriaList\n )\n\n GENERATION_TYPES = {\n \"top_k\": TopKLogitsWarper,\n \"top_p\": TopPLogitsWarper,\n \"beam_search\": \"beam_search\"\n }\n _has_transformers = True\nexcept ImportError as e:\n GENERATION_TYPES = {\n \"top_k\": None,\n \"top_p\": None,\n \"beam_search\": \"beam_search\"\n }\n _has_transformers = False\n\n\n@dataclass\nclass MultimodalCfg(CLIPTextCfg):\n mlp_ratio: int = 4\n dim_head: int = 64\n heads: int = 8\n n_queries: int = 256\n attn_pooler_heads: int = 8\n\n\ndef _build_text_decoder_tower(\n embed_dim,\n multimodal_cfg,\n quick_gelu: bool = False,\n cast_dtype: Optional[torch.dtype] = None,\n):\n multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg\n act_layer = QuickGELU if quick_gelu else nn.GELU\n norm_layer = (\n LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm\n )\n\n decoder = MultimodalTransformer(\n context_length=multimodal_cfg.context_length,\n width=multimodal_cfg.width,\n heads=multimodal_cfg.heads,\n layers=multimodal_cfg.layers,\n ls_init_value=multimodal_cfg.ls_init_value,\n output_dim=embed_dim,\n act_layer=act_layer,\n norm_layer=norm_layer,\n )\n\n return decoder\n\n\nclass CoCa(nn.Module):\n def __init__(\n self,\n embed_dim,\n multimodal_cfg: MultimodalCfg,\n text_cfg: CLIPTextCfg,\n vision_cfg: CLIPVisionCfg,\n quick_gelu: bool = False,\n cast_dtype: Optional[torch.dtype] = None,\n pad_id: int = 0,\n ):\n super().__init__()\n multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg\n text_cfg = CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg\n vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(vision_cfg, dict) else vision_cfg\n\n self.text = _build_text_tower(\n embed_dim=embed_dim,\n text_cfg=text_cfg,\n quick_gelu=quick_gelu,\n cast_dtype=cast_dtype,\n )\n\n vocab_size = (\n text_cfg.vocab_size # for hf models\n if hasattr(text_cfg, \"hf_model_name\") and text_cfg.hf_model_name is not None\n else text_cfg.vocab_size\n )\n\n self.visual = _build_vision_tower(\n embed_dim=embed_dim,\n vision_cfg=vision_cfg,\n quick_gelu=quick_gelu,\n cast_dtype=cast_dtype,\n )\n\n self.text_decoder = _build_text_decoder_tower(\n vocab_size,\n multimodal_cfg=multimodal_cfg,\n quick_gelu=quick_gelu,\n cast_dtype=cast_dtype,\n )\n\n self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n self.pad_id = pad_id\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.visual.set_grad_checkpointing(enable)\n self.text.set_grad_checkpointing(enable)\n self.text_decoder.set_grad_checkpointing(enable)\n\n def _encode_image(self, images, normalize=True):\n image_latent, tokens_embs = self.visual(images)\n image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent\n return image_latent, tokens_embs\n\n def _encode_text(self, text, normalize=True, embed_cls=True):\n text = text[:, :-1] if embed_cls else text # make space for CLS token\n text_latent, token_emb = self.text(text)\n text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent\n return text_latent, token_emb\n\n def encode_image(self, images, normalize=True):\n image_latent, _ = self._encode_image(images, normalize=normalize)\n return image_latent\n\n def encode_text(self, text, normalize=True, embed_cls=True):\n text_latent, _ = self._encode_text(text, normalize=normalize, embed_cls=embed_cls)\n return text_latent\n\n def forward(self, image, text, embed_cls=True, image_latent=None, image_embs=None):\n text_latent, token_embs = self._encode_text(text, embed_cls=embed_cls)\n if image_latent is None or image_embs is None:\n image_latent, image_embs = self._encode_image(image)\n\n # TODO: add assertion to avoid bugs?\n labels = text[:, -token_embs.shape[1]:]\n\n logits = self.text_decoder(image_embs, token_embs)\n return {\n \"image_features\": image_latent,\n \"text_features\": text_latent,\n \"logits\": logits,\n \"labels\": labels,\n \"logit_scale\": self.logit_scale.exp()\n }\n\n def generate(\n self,\n image,\n text=None,\n seq_len=30,\n max_seq_len=77,\n temperature=1.,\n generation_type=\"beam_search\",\n top_p=0.1, # keep tokens in the 1 - top_p quantile\n top_k=1, # keeps the top_k most probable tokens\n pad_token_id=None,\n eos_token_id=None,\n sot_token_id=None,\n num_beams=6,\n num_beam_groups=3,\n min_seq_len=5,\n stopping_criteria=None,\n repetition_penalty=1.0,\n fixed_output_length=False # if True output.shape == (batch_size, seq_len)\n ):\n # taking many ideas and components from HuggingFace GenerationMixin\n # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation\n assert _has_transformers, \"Please install transformers for generate functionality. `pip install transformers`.\"\n assert seq_len > min_seq_len, \"seq_len must be larger than min_seq_len\"\n\n with torch.no_grad():\n sot_token_id = 49406 if sot_token_id is None else sot_token_id\n eos_token_id = 49407 if eos_token_id is None else eos_token_id\n pad_token_id = self.pad_id if pad_token_id is None else pad_token_id\n logit_processor = LogitsProcessorList(\n [\n MinLengthLogitsProcessor(min_seq_len, eos_token_id),\n RepetitionPenaltyLogitsProcessor(repetition_penalty),\n ]\n )\n\n if stopping_criteria is None:\n stopping_criteria = [MaxLengthCriteria(max_length=seq_len)]\n\n stopping_criteria = StoppingCriteriaList(\n stopping_criteria\n )\n\n device = image.device\n\n if generation_type == \"beam_search\":\n output = self._generate_beamsearch(\n image_inputs = image,\n pad_token_id=pad_token_id,\n eos_token_id=eos_token_id,\n sot_token_id=sot_token_id,\n num_beams=num_beams,\n num_beam_groups=num_beam_groups,\n min_seq_len=min_seq_len,\n stopping_criteria=stopping_criteria,\n logit_processor=logit_processor,\n )\n if fixed_output_length and output.shape[1] < seq_len:\n return torch.cat(\n (output, torch.ones(output.shape[0], seq_len-output.shape[1], device=device, dtype=output.dtype) * self.pad_id),\n dim=1\n )\n return output\n\n elif generation_type == \"top_p\":\n logit_warper = GENERATION_TYPES[generation_type](top_p)\n elif generation_type == \"top_k\":\n logit_warper = GENERATION_TYPES[generation_type](top_k)\n else:\n raise ValueError(\n f\"generation_type has to be one of \"\n f\"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}.\"\n )\n\n image_latent, image_embs = self._encode_image(image)\n\n if text is None:\n text = torch.ones((image.shape[0], 1), device=device, dtype=torch.long) * sot_token_id\n\n was_training = self.training\n num_dims = len(text.shape)\n\n if num_dims == 1:\n text = text[None, :]\n\n cur_len = text.shape[1]\n self.eval()\n out = text\n\n while True:\n x = out[:, -max_seq_len:]\n cur_len = x.shape[1]\n logits = self(image, x, image_latent=image_latent, image_embs=image_embs, embed_cls=False)[\"logits\"][:, -1]\n mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id)\n sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id\n\n if mask.all():\n if not fixed_output_length:\n break\n else:\n logits = logits[~mask, :]\n filtered_logits = logit_processor(x[~mask, :], logits)\n filtered_logits = logit_warper(x[~mask, :], filtered_logits)\n probs = F.softmax(filtered_logits / temperature, dim=-1)\n\n if (cur_len + 1 == seq_len):\n sample[~mask, :] = torch.ones((sum(~mask), 1), device=device, dtype=torch.long) * eos_token_id\n else:\n sample[~mask, :] = torch.multinomial(probs, 1)\n\n out = torch.cat((out, sample), dim=-1)\n\n cur_len += 1\n\n if stopping_criteria(out, None):\n break\n\n if num_dims == 1:\n out = out.squeeze(0)\n\n self.train(was_training)\n return out\n\n def _generate_beamsearch(\n self,\n image_inputs,\n pad_token_id=None,\n eos_token_id=None,\n sot_token_id=None,\n num_beams=6,\n num_beam_groups=3,\n min_seq_len=5,\n stopping_criteria=None,\n logit_processor=None,\n logit_warper=None,\n ):\n device = image_inputs.device\n batch_size = image_inputs.shape[0]\n image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0)\n image_latent, image_embs = self._encode_image(image_inputs)\n\n input_ids = torch.ones((batch_size * num_beams, 1), device=device, dtype=torch.long)\n input_ids = input_ids * sot_token_id\n beam_scorer = BeamSearchScorer(\n batch_size=batch_size,\n num_beams=num_beams,\n device=device,\n num_beam_groups=num_beam_groups,\n )\n # instantiate logits processors\n logits_processor = (\n LogitsProcessorList([MinLengthLogitsProcessor(min_seq_len, eos_token_id=eos_token_id)])\n if logit_processor is None\n else logit_processor\n )\n\n batch_size = len(beam_scorer._beam_hyps)\n num_beams = beam_scorer.num_beams\n num_beam_groups = beam_scorer.num_beam_groups\n num_sub_beams = num_beams // num_beam_groups\n batch_beam_size, cur_len = input_ids.shape\n beam_indices = None\n\n if num_beams * batch_size != batch_beam_size:\n raise ValueError(\n f\"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}.\"\n )\n\n beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)\n # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in\n # the same group don't produce same tokens everytime.\n beam_scores[:, ::num_sub_beams] = 0\n beam_scores = beam_scores.view((batch_size * num_beams,))\n\n while True:\n\n # predicted tokens in cur_len step\n current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)\n\n # indices which will form the beams in the next time step\n reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)\n\n # do one decoder step on all beams of all sentences in batch\n model_inputs = prepare_inputs_for_generation(input_ids=input_ids, image_inputs=image_inputs)\n outputs = self(\n model_inputs['images'],\n model_inputs['text'],\n embed_cls=False,\n image_latent=image_latent,\n image_embs=image_embs\n )\n\n for beam_group_idx in range(num_beam_groups):\n group_start_idx = beam_group_idx * num_sub_beams\n group_end_idx = min(group_start_idx + num_sub_beams, num_beams)\n group_size = group_end_idx - group_start_idx\n\n # indices of beams of current group among all sentences in batch\n batch_group_indices = []\n\n for batch_idx in range(batch_size):\n batch_group_indices.extend(\n [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]\n )\n group_input_ids = input_ids[batch_group_indices]\n\n # select outputs of beams of currentg group only\n next_token_logits = outputs['logits'][batch_group_indices, -1, :]\n vocab_size = next_token_logits.shape[-1]\n\n next_token_scores_processed = logits_processor(\n group_input_ids, next_token_logits, current_tokens=current_tokens, beam_group_idx=beam_group_idx\n )\n next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)\n next_token_scores = next_token_scores.expand_as(next_token_scores_processed)\n\n # reshape for beam search\n next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)\n\n next_token_scores, next_tokens = torch.topk(\n next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True\n )\n\n next_indices = torch.div(next_tokens, vocab_size, rounding_mode=\"floor\")\n next_tokens = next_tokens % vocab_size\n\n # stateless\n process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None\n beam_outputs = beam_scorer.process(\n group_input_ids,\n next_token_scores,\n next_tokens,\n next_indices,\n pad_token_id=pad_token_id,\n eos_token_id=eos_token_id,\n beam_indices=process_beam_indices,\n )\n beam_scores[batch_group_indices] = beam_outputs[\"next_beam_scores\"]\n beam_next_tokens = beam_outputs[\"next_beam_tokens\"]\n beam_idx = beam_outputs[\"next_beam_indices\"]\n\n input_ids[batch_group_indices] = group_input_ids[beam_idx]\n group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)\n current_tokens[batch_group_indices] = group_input_ids[:, -1]\n\n # (beam_idx // group_size) -> batch_idx\n # (beam_idx % group_size) -> offset of idx inside the group\n reordering_indices[batch_group_indices] = (\n num_beams * torch.div(beam_idx, group_size, rounding_mode=\"floor\") + group_start_idx + (beam_idx % group_size)\n )\n\n input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)\n\n # increase cur_len\n cur_len = cur_len + 1\n if beam_scorer.is_done or stopping_criteria(input_ids, None):\n break\n\n final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None\n sequence_outputs = beam_scorer.finalize(\n input_ids,\n beam_scores,\n next_tokens,\n next_indices,\n pad_token_id=pad_token_id,\n eos_token_id=eos_token_id,\n max_length=stopping_criteria.max_length,\n beam_indices=final_beam_indices,\n )\n return sequence_outputs['sequences']\n\n\ndef prepare_inputs_for_generation(input_ids, image_inputs, past=None, **kwargs):\n if past:\n input_ids = input_ids[:, -1].unsqueeze(-1)\n\n attention_mask = kwargs.get(\"attention_mask\", None)\n position_ids = kwargs.get(\"position_ids\", None)\n\n if attention_mask is not None and position_ids is None:\n # create position_ids on the fly for batch generation\n position_ids = attention_mask.long().cumsum(-1) - 1\n position_ids.masked_fill_(attention_mask == 0, 1)\n else:\n position_ids = None\n return {\n \"text\": input_ids,\n \"images\": image_inputs,\n \"past_key_values\": past,\n \"position_ids\": position_ids,\n \"attention_mask\": attention_mask,\n }\n"
},
{
"path": "src/open_clip/constants.py",
"content": "OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)\nOPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)\n"
},
{
"path": "src/open_clip/factory.py",
"content": "import json\nimport logging\nimport os\nimport pathlib\nimport re\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom typing import Any, Dict, Optional, Tuple, Union\n\nimport torch\n\nfrom .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD\nfrom .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\\\n resize_pos_embed, get_cast_dtype\nfrom .coca_model import CoCa\nfrom .loss import ClipLoss, DistillClipLoss, CoCaLoss\nfrom .openai import load_openai_model\nfrom .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model, download_pretrained_from_hf\nfrom .transform import image_transform, AugmentationCfg\nfrom .tokenizer import HFTokenizer, tokenize\n\n\nHF_HUB_PREFIX = 'hf-hub:'\n_MODEL_CONFIG_PATHS = [Path(__file__).parent / f\"model_configs/\"]\n_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs\n\n\ndef _natural_key(string_):\n return [int(s) if s.isdigit() else s for s in re.split(r'(\\d+)', string_.lower())]\n\n\ndef _rescan_model_configs():\n global _MODEL_CONFIGS\n\n config_ext = ('.json',)\n config_files = []\n for config_path in _MODEL_CONFIG_PATHS:\n if config_path.is_file() and config_path.suffix in config_ext:\n config_files.append(config_path)\n elif config_path.is_dir():\n for ext in config_ext:\n config_files.extend(config_path.glob(f'*{ext}'))\n\n for cf in config_files:\n with open(cf, 'r') as f:\n model_cfg = json.load(f)\n if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):\n _MODEL_CONFIGS[cf.stem] = model_cfg\n\n _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}\n\n\n_rescan_model_configs() # initial populate of model config registry\n\n\ndef list_models():\n \"\"\" enumerate available model architectures based on config files \"\"\"\n return list(_MODEL_CONFIGS.keys())\n\n\ndef add_model_config(path):\n \"\"\" add model config path or file and update registry \"\"\"\n if not isinstance(path, Path):\n path = Path(path)\n _MODEL_CONFIG_PATHS.append(path)\n _rescan_model_configs()\n\n\ndef get_model_config(model_name):\n if model_name in _MODEL_CONFIGS:\n return deepcopy(_MODEL_CONFIGS[model_name])\n else:\n return None\n\n\ndef get_tokenizer(model_name):\n if model_name.startswith(HF_HUB_PREFIX):\n tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):])\n else:\n config = get_model_config(model_name)\n tokenizer = HFTokenizer(\n config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize\n return tokenizer\n\n\ndef load_state_dict(checkpoint_path: str, map_location='cpu'):\n checkpoint = torch.load(checkpoint_path, map_location=map_location)\n if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:\n state_dict = checkpoint['state_dict']\n else:\n state_dict = checkpoint\n if next(iter(state_dict.items()))[0].startswith('module'):\n state_dict = {k[7:]: v for k, v in state_dict.items()}\n return state_dict\n\n\ndef load_checkpoint(model, checkpoint_path, strict=True):\n state_dict = load_state_dict(checkpoint_path)\n # detect old format and make compatible with new format\n if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):\n state_dict = convert_to_custom_text_state_dict(state_dict)\n resize_pos_embed(state_dict, model)\n incompatible_keys = model.load_state_dict(state_dict, strict=strict)\n return incompatible_keys\n\n\ndef create_model(\n model_name: str,\n pretrained: Optional[str] = None,\n precision: str = 'fp32',\n device: Union[str, torch.device] = 'cpu',\n jit: bool = False,\n force_quick_gelu: bool = False,\n force_custom_text: bool = False,\n force_patch_dropout: Optional[float] = None,\n force_image_size: Optional[Union[int, Tuple[int, int]]] = None,\n pretrained_image: bool = False,\n pretrained_hf: bool = True,\n cache_dir: Optional[str] = None,\n output_dict: Optional[bool] = None,\n require_pretrained: bool = False,\n):\n has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)\n if has_hf_hub_prefix:\n model_id = model_name[len(HF_HUB_PREFIX):]\n checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)\n config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)\n\n with open(config_path, 'r', encoding='utf-8') as f:\n config = json.load(f)\n pretrained_cfg = config['preprocess_cfg']\n model_cfg = config['model_cfg']\n else:\n model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names\n checkpoint_path = None\n pretrained_cfg = {}\n model_cfg = None\n\n if isinstance(device, str):\n device = torch.device(device)\n\n if pretrained and pretrained.lower() == 'openai':\n logging.info(f'Loading pretrained {model_name} from OpenAI.')\n model = load_openai_model(\n model_name,\n precision=precision,\n device=device,\n jit=jit,\n cache_dir=cache_dir,\n )\n\n # to always output dict even if it is clip\n if output_dict and hasattr(model, \"output_dict\"):\n model.output_dict = True\n else:\n model_cfg = model_cfg or get_model_config(model_name)\n if model_cfg is not None:\n logging.info(f'Loaded {model_name} model config.')\n else:\n logging.error(f'Model config for {model_name} not found; available models {list_models()}.')\n raise RuntimeError(f'Model config for {model_name} not found.')\n\n if force_quick_gelu:\n # override for use of QuickGELU on non-OpenAI transformer models\n model_cfg[\"quick_gelu\"] = True\n\n if force_patch_dropout is not None:\n # override the default patch dropout value\n model_cfg[\"vision_cfg\"][\"patch_dropout\"] = force_patch_dropout\n\n if force_image_size is not None:\n # override model config's image size\n model_cfg[\"vision_cfg\"][\"image_size\"] = force_image_size\n\n if pretrained_image:\n if 'timm_model_name' in model_cfg.get('vision_cfg', {}):\n # pretrained weight loading for timm models set via vision_cfg\n model_cfg['vision_cfg']['timm_model_pretrained'] = True\n else:\n assert False, 'pretrained image towers currently only supported for timm models'\n\n cast_dtype = get_cast_dtype(precision)\n is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})\n custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model\n\n if custom_text:\n if is_hf_model:\n model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf\n if \"coca\" in model_name:\n model = CoCa(**model_cfg, cast_dtype=cast_dtype)\n else:\n model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)\n else:\n model = CLIP(**model_cfg, cast_dtype=cast_dtype)\n\n pretrained_loaded = False\n if pretrained:\n checkpoint_path = ''\n pretrained_cfg = get_pretrained_cfg(model_name, pretrained)\n if pretrained_cfg:\n checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)\n elif os.path.exists(pretrained):\n checkpoint_path = pretrained\n\n if checkpoint_path:\n logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')\n load_checkpoint(model, checkpoint_path)\n else:\n error_str = (\n f'Pretrained weights ({pretrained}) not found for model {model_name}.'\n f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')\n logging.warning(error_str)\n raise RuntimeError(error_str)\n pretrained_loaded = True\n elif has_hf_hub_prefix:\n logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')\n load_checkpoint(model, checkpoint_path)\n pretrained_loaded = True\n\n if require_pretrained and not pretrained_loaded:\n # callers of create_model_from_pretrained always expect pretrained weights\n raise RuntimeError(\n f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.')\n\n model.to(device=device)\n if precision in (\"fp16\", \"bf16\"):\n convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16)\n\n # set image / mean metadata from pretrained_cfg if available, or use default\n model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN\n model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD\n\n # to always output dict even if it is clip\n if output_dict and hasattr(model, \"output_dict\"):\n model.output_dict = True\n\n if jit:\n model = torch.jit.script(model)\n\n return model\n\n\ndef create_loss(args):\n if args.distill:\n return DistillClipLoss(\n local_loss=args.local_loss,\n gather_with_grad=args.gather_with_grad,\n cache_labels=True,\n rank=args.rank,\n world_size=args.world_size,\n use_horovod=args.horovod,\n )\n elif \"coca\" in args.model.lower():\n return CoCaLoss(\n caption_loss_weight=args.coca_caption_loss_weight,\n clip_loss_weight=args.coca_contrastive_loss_weight,\n local_loss=args.local_loss,\n gather_with_grad=args.gather_with_grad,\n cache_labels=True,\n rank=args.rank,\n world_size=args.world_size,\n use_horovod=args.horovod,\n )\n return ClipLoss(\n local_loss=args.local_loss,\n gather_with_grad=args.gather_with_grad,\n cache_labels=True,\n rank=args.rank,\n world_size=args.world_size,\n use_horovod=args.horovod,\n )\n\n\ndef create_model_and_transforms(\n model_name: str,\n pretrained: Optional[str] = None,\n precision: str = 'fp32',\n device: Union[str, torch.device] = 'cpu',\n jit: bool = False,\n force_quick_gelu: bool = False,\n force_custom_text: bool = False,\n force_patch_dropout: Optional[float] = None,\n force_image_size: Optional[Union[int, Tuple[int, int]]] = None,\n pretrained_image: bool = False,\n pretrained_hf: bool = True,\n image_mean: Optional[Tuple[float, ...]] = None,\n image_std: Optional[Tuple[float, ...]] = None,\n aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,\n cache_dir: Optional[str] = None,\n output_dict: Optional[bool] = None,\n):\n model = create_model(\n model_name,\n pretrained,\n precision=precision,\n device=device,\n jit=jit,\n force_quick_gelu=force_quick_gelu,\n force_custom_text=force_custom_text,\n force_patch_dropout=force_patch_dropout,\n force_image_size=force_image_size,\n pretrained_image=pretrained_image,\n pretrained_hf=pretrained_hf,\n cache_dir=cache_dir,\n output_dict=output_dict,\n )\n\n image_mean = image_mean or getattr(model.visual, 'image_mean', None)\n image_std = image_std or getattr(model.visual, 'image_std', None)\n preprocess_train = image_transform(\n model.visual.image_size,\n is_train=True,\n mean=image_mean,\n std=image_std,\n aug_cfg=aug_cfg,\n )\n preprocess_val = image_transform(\n model.visual.image_size,\n is_train=False,\n mean=image_mean,\n std=image_std,\n )\n\n return model, preprocess_train, preprocess_val\n\n\ndef create_model_from_pretrained(\n model_name: str,\n pretrained: Optional[str] = None,\n precision: str = 'fp32',\n device: Union[str, torch.device] = 'cpu',\n jit: bool = False,\n force_quick_gelu: bool = False,\n force_custom_text: bool = False,\n force_image_size: Optional[Union[int, Tuple[int, int]]] = None,\n return_transform: bool = True,\n image_mean: Optional[Tuple[float, ...]] = None,\n image_std: Optional[Tuple[float, ...]] = None,\n cache_dir: Optional[str] = None,\n):\n model = create_model(\n model_name,\n pretrained,\n precision=precision,\n device=device,\n jit=jit,\n force_quick_gelu=force_quick_gelu,\n force_custom_text=force_custom_text,\n force_image_size=force_image_size,\n cache_dir=cache_dir,\n require_pretrained=True,\n )\n\n if not return_transform:\n return model\n\n image_mean = image_mean or getattr(model.visual, 'image_mean', None)\n image_std = image_std or getattr(model.visual, 'image_std', None)\n preprocess = image_transform(\n model.visual.image_size,\n is_train=False,\n mean=image_mean,\n std=image_std,\n )\n\n return model, preprocess\n"
},
{
"path": "src/open_clip/generation_utils.py",
"content": ""
},
{
"path": "src/open_clip/hf_configs.py",
"content": "# HF architecture dict:\narch_dict = {\n # https://huggingface.co/docs/transformers/model_doc/roberta#roberta\n \"roberta\": {\n \"config_names\": {\n \"context_length\": \"max_position_embeddings\",\n \"vocab_size\": \"vocab_size\",\n \"width\": \"hidden_size\",\n \"heads\": \"num_attention_heads\",\n \"layers\": \"num_hidden_layers\",\n \"layer_attr\": \"layer\",\n \"token_embeddings_attr\": \"embeddings\"\n },\n \"pooler\": \"mean_pooler\",\n },\n # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig\n \"xlm-roberta\": {\n \"config_names\": {\n \"context_length\": \"max_position_embeddings\",\n \"vocab_size\": \"vocab_size\",\n \"width\": \"hidden_size\",\n \"heads\": \"num_attention_heads\",\n \"layers\": \"num_hidden_layers\",\n \"layer_attr\": \"layer\",\n \"token_embeddings_attr\": \"embeddings\"\n },\n \"pooler\": \"mean_pooler\",\n },\n # https://huggingface.co/docs/transformers/model_doc/mt5#mt5\n \"mt5\": {\n \"config_names\": {\n # unlimited seqlen\n # https://github.com/google-research/text-to-text-transfer-transformer/issues/273\n # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374\n \"context_length\": \"\",\n \"vocab_size\": \"vocab_size\",\n \"width\": \"d_model\",\n \"heads\": \"num_heads\",\n \"layers\": \"num_layers\",\n \"layer_attr\": \"block\",\n \"token_embeddings_attr\": \"embed_tokens\"\n },\n \"pooler\": \"mean_pooler\",\n },\n}\n"
},
{
"path": "src/open_clip/hf_model.py",
"content": "\"\"\" huggingface model adapter\n\nWraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model.\n\"\"\"\n\nimport re\n\nimport torch\nimport torch.nn as nn\nfrom torch import TensorType\n\ntry:\n import transformers\n from transformers import AutoModel, AutoTokenizer, AutoConfig, PretrainedConfig\n from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \\\n BaseModelOutputWithPoolingAndCrossAttentions\nexcept ImportError as e:\n transformers = None\n\n\n class BaseModelOutput:\n pass\n\n\n class PretrainedConfig:\n pass\n\nfrom .hf_configs import arch_dict\n\n\n# utils\ndef _camel2snake(s):\n return re.sub(r'(? torch.Tensor:\n # calculated ground-truth and cache if enabled\n if self.prev_num_logits != num_logits or device not in self.labels:\n labels = torch.arange(num_logits, device=device, dtype=torch.long)\n if self.world_size > 1 and self.local_loss:\n labels = labels + num_logits * self.rank\n if self.cache_labels:\n self.labels[device] = labels\n self.prev_num_logits = num_logits\n else:\n labels = self.labels[device]\n return labels\n\n def get_logits(self, image_features, text_features, logit_scale):\n if self.world_size > 1:\n all_image_features, all_text_features = gather_features(\n image_features, text_features,\n self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod)\n\n if self.local_loss:\n logits_per_image = logit_scale * image_features @ all_text_features.T\n logits_per_text = logit_scale * text_features @ all_image_features.T\n else:\n logits_per_image = logit_scale * all_image_features @ all_text_features.T\n logits_per_text = logits_per_image.T\n else:\n logits_per_image = logit_scale * image_features @ text_features.T\n logits_per_text = logit_scale * text_features @ image_features.T\n \n return logits_per_image, logits_per_text\n\n def forward(self, image_features, text_features, logit_scale, output_dict=False):\n device = image_features.device\n logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale)\n\n labels = self.get_ground_truth(device, logits_per_image.shape[0])\n\n total_loss = (\n F.cross_entropy(logits_per_image, labels) +\n F.cross_entropy(logits_per_text, labels)\n ) / 2\n\n return {\"contrastive_loss\": total_loss} if output_dict else total_loss\n\n\nclass CoCaLoss(ClipLoss):\n def __init__(\n self,\n caption_loss_weight,\n clip_loss_weight,\n pad_id=0, # pad_token for open_clip custom tokenizer\n local_loss=False,\n gather_with_grad=False,\n cache_labels=False,\n rank=0,\n world_size=1,\n use_horovod=False,\n ):\n super().__init__(\n local_loss=local_loss,\n gather_with_grad=gather_with_grad,\n cache_labels=cache_labels,\n rank=rank,\n world_size=world_size,\n use_horovod=use_horovod\n )\n\n self.clip_loss_weight = clip_loss_weight\n self.caption_loss_weight = caption_loss_weight\n self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id)\n\n def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False):\n clip_loss = super().forward(image_features, text_features, logit_scale)\n clip_loss = self.clip_loss_weight * clip_loss\n\n caption_loss = self.caption_loss(\n logits.permute(0, 2, 1),\n labels,\n )\n caption_loss = caption_loss * self.caption_loss_weight\n\n if output_dict:\n return {\"contrastive_loss\": clip_loss, \"caption_loss\": caption_loss}\n\n return clip_loss, caption_loss\n\n\nclass DistillClipLoss(ClipLoss):\n\n def dist_loss(self, teacher_logits, student_logits):\n return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0)\n\n def forward(\n self,\n image_features,\n text_features,\n logit_scale,\n dist_image_features,\n dist_text_features,\n dist_logit_scale,\n output_dict=False,\n ):\n logits_per_image, logits_per_text = \\\n self.get_logits(image_features, text_features, logit_scale)\n\n dist_logits_per_image, dist_logits_per_text = \\\n self.get_logits(dist_image_features, dist_text_features, dist_logit_scale)\n\n labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0])\n\n contrastive_loss = (\n F.cross_entropy(logits_per_image, labels) +\n F.cross_entropy(logits_per_text, labels)\n ) / 2\n\n distill_loss = (\n self.dist_loss(dist_logits_per_image, logits_per_image) +\n self.dist_loss(dist_logits_per_text, logits_per_text)\n ) / 2\n\n if output_dict:\n return {\"contrastive_loss\": contrastive_loss, \"distill_loss\": distill_loss}\n\n return contrastive_loss, distill_loss\n"
},
{
"path": "src/open_clip/model.py",
"content": "\"\"\" CLIP Model\n\nAdapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.\n\"\"\"\nfrom dataclasses import dataclass\nimport logging\nimport math\nfrom typing import Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torch.utils.checkpoint import checkpoint\n\nfrom .hf_model import HFTextEncoder\nfrom .modified_resnet import ModifiedResNet\nfrom .timm_model import TimmModel\nfrom .transformer import LayerNormFp32, LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer\nfrom .utils import to_2tuple\n\n\n@dataclass\nclass CLIPVisionCfg:\n layers: Union[Tuple[int, int, int, int], int] = 12\n width: int = 768\n head_width: int = 64\n mlp_ratio: float = 4.0\n patch_size: int = 16\n image_size: Union[Tuple[int, int], int] = 224\n ls_init_value: Optional[float] = None # layer scale initial value\n patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results\n input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design\n global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)\n attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer\n n_queries: int = 256 # n_queries for attentional pooler\n attn_pooler_heads: int = 8 # n heads for attentional_pooling\n timm_model_name: str = None # a valid model name overrides layers, width, patch_size\n timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model\n timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')\n timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')\n timm_proj_bias: bool = False # enable bias final projection\n timm_drop: float = 0. # head dropout\n timm_drop_path: Optional[float] = None # backbone stochastic depth\n output_tokens: bool = False\n\n\n@dataclass\nclass CLIPTextCfg:\n context_length: int = 77\n vocab_size: int = 49408\n width: int = 512\n heads: int = 8\n layers: int = 12\n ls_init_value: Optional[float] = None # layer scale initial value\n hf_model_name: str = None\n hf_tokenizer_name: str = None\n hf_model_pretrained: bool = True\n proj: str = 'mlp'\n pooler_type: str = 'mean_pooler'\n embed_cls: bool = False\n pad_id: int = 0\n output_tokens: bool = False\n\n\ndef get_cast_dtype(precision: str):\n cast_dtype = None\n if precision == 'bf16':\n cast_dtype = torch.bfloat16\n elif precision == 'fp16':\n cast_dtype = torch.float16\n return cast_dtype\n\n\ndef _build_vision_tower(\n embed_dim: int,\n vision_cfg: CLIPVisionCfg,\n quick_gelu: bool = False,\n cast_dtype: Optional[torch.dtype] = None\n):\n if isinstance(vision_cfg, dict):\n vision_cfg = CLIPVisionCfg(**vision_cfg)\n\n # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more\n # memory efficient in recent PyTorch releases (>= 1.10).\n # NOTE: timm models always use native GELU regardless of quick_gelu flag.\n act_layer = QuickGELU if quick_gelu else nn.GELU\n\n if vision_cfg.timm_model_name:\n visual = TimmModel(\n vision_cfg.timm_model_name,\n pretrained=vision_cfg.timm_model_pretrained,\n pool=vision_cfg.timm_pool,\n proj=vision_cfg.timm_proj,\n proj_bias=vision_cfg.timm_proj_bias,\n drop=vision_cfg.timm_drop,\n drop_path=vision_cfg.timm_drop_path,\n embed_dim=embed_dim,\n image_size=vision_cfg.image_size,\n )\n act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models\n elif isinstance(vision_cfg.layers, (tuple, list)):\n vision_heads = vision_cfg.width * 32 // vision_cfg.head_width\n visual = ModifiedResNet(\n layers=vision_cfg.layers,\n output_dim=embed_dim,\n heads=vision_heads,\n image_size=vision_cfg.image_size,\n width=vision_cfg.width,\n )\n else:\n vision_heads = vision_cfg.width // vision_cfg.head_width\n norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm\n visual = VisionTransformer(\n image_size=vision_cfg.image_size,\n patch_size=vision_cfg.patch_size,\n width=vision_cfg.width,\n layers=vision_cfg.layers,\n heads=vision_heads,\n mlp_ratio=vision_cfg.mlp_ratio,\n ls_init_value=vision_cfg.ls_init_value,\n patch_dropout=vision_cfg.patch_dropout,\n input_patchnorm=vision_cfg.input_patchnorm,\n global_average_pool=vision_cfg.global_average_pool,\n attentional_pool=vision_cfg.attentional_pool,\n n_queries=vision_cfg.n_queries,\n attn_pooler_heads=vision_cfg.attn_pooler_heads,\n output_tokens=vision_cfg.output_tokens,\n output_dim=embed_dim,\n act_layer=act_layer,\n norm_layer=norm_layer,\n )\n\n return visual\n\n\ndef _build_text_tower(\n embed_dim: int,\n text_cfg: CLIPTextCfg,\n quick_gelu: bool = False,\n cast_dtype: Optional[torch.dtype] = None,\n):\n if isinstance(text_cfg, dict):\n text_cfg = CLIPTextCfg(**text_cfg)\n\n if text_cfg.hf_model_name:\n text = HFTextEncoder(\n text_cfg.hf_model_name,\n output_dim=embed_dim,\n proj=text_cfg.proj,\n pooler_type=text_cfg.pooler_type,\n pretrained=text_cfg.hf_model_pretrained,\n output_tokens=text_cfg.output_tokens,\n )\n else:\n act_layer = QuickGELU if quick_gelu else nn.GELU\n norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm\n\n text = TextTransformer(\n context_length=text_cfg.context_length,\n vocab_size=text_cfg.vocab_size,\n width=text_cfg.width,\n heads=text_cfg.heads,\n layers=text_cfg.layers,\n ls_init_value=text_cfg.ls_init_value,\n output_dim=embed_dim,\n embed_cls=text_cfg.embed_cls,\n output_tokens=text_cfg.output_tokens,\n pad_id=text_cfg.pad_id,\n act_layer=act_layer,\n norm_layer=norm_layer,\n )\n return text\n\n\nclass CLIP(nn.Module):\n output_dict: torch.jit.Final[bool]\n\n def __init__(\n self,\n embed_dim: int,\n vision_cfg: CLIPVisionCfg,\n text_cfg: CLIPTextCfg,\n quick_gelu: bool = False,\n cast_dtype: Optional[torch.dtype] = None,\n output_dict: bool = False,\n ):\n super().__init__()\n self.output_dict = output_dict\n self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)\n\n text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)\n self.transformer = text.transformer\n self.vocab_size = text.vocab_size\n self.token_embedding = text.token_embedding\n self.positional_embedding = text.positional_embedding\n self.ln_final = text.ln_final\n self.text_projection = text.text_projection\n self.register_buffer('attn_mask', text.attn_mask, persistent=False)\n\n self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n\n def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):\n # lock image tower as per LiT - https://arxiv.org/abs/2111.07991\n self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.visual.set_grad_checkpointing(enable)\n self.transformer.grad_checkpointing = enable\n\n def encode_image(self, image, normalize: bool = False):\n features = self.visual(image)\n return F.normalize(features, dim=-1) if normalize else features\n\n def encode_text(self, text, normalize: bool = False):\n cast_dtype = self.transformer.get_cast_dtype()\n\n x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model]\n\n x = x + self.positional_embedding.to(cast_dtype)\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x, attn_mask=self.attn_mask)\n x = x.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x) # [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection\n return F.normalize(x, dim=-1) if normalize else x\n\n def forward(self, image, text):\n image_features = self.encode_image(image, normalize=True)\n text_features = self.encode_text(text, normalize=True)\n if self.output_dict:\n return {\n \"image_features\": image_features,\n \"text_features\": text_features,\n \"logit_scale\": self.logit_scale.exp()\n }\n return image_features, text_features, self.logit_scale.exp()\n\n\nclass CustomTextCLIP(nn.Module):\n output_dict: torch.jit.Final[bool]\n\n def __init__(\n self,\n embed_dim: int,\n vision_cfg: CLIPVisionCfg,\n text_cfg: CLIPTextCfg,\n quick_gelu: bool = False,\n cast_dtype: Optional[torch.dtype] = None,\n output_dict: bool = False,\n ):\n super().__init__()\n self.output_dict = output_dict\n self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)\n self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)\n self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))\n\n def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):\n # lock image tower as per LiT - https://arxiv.org/abs/2111.07991\n self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)\n\n def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):\n self.text.lock(unlocked_layers, freeze_layer_norm)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.visual.set_grad_checkpointing(enable)\n self.text.set_grad_checkpointing(enable)\n\n def encode_image(self, image, normalize: bool = False):\n features = self.visual(image)\n return F.normalize(features, dim=-1) if normalize else features\n\n def encode_text(self, text, normalize: bool = False):\n features = self.text(text)\n return F.normalize(features, dim=-1) if normalize else features\n\n def forward(self, image, text):\n image_features = self.encode_image(image, normalize=True)\n text_features = self.encode_text(text, normalize=True)\n if self.output_dict:\n return {\n \"image_features\": image_features,\n \"text_features\": text_features,\n \"logit_scale\": self.logit_scale.exp()\n }\n return image_features, text_features, self.logit_scale.exp()\n\n\ndef convert_weights_to_lp(model: nn.Module, dtype=torch.float16):\n \"\"\"Convert applicable model parameters to low-precision (bf16 or fp16)\"\"\"\n\n def _convert_weights(l):\n if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):\n l.weight.data = l.weight.data.to(dtype)\n if l.bias is not None:\n l.bias.data = l.bias.data.to(dtype)\n\n if isinstance(l, (nn.MultiheadAttention, Attention)):\n for attr in [*[f\"{s}_proj_weight\" for s in [\"in\", \"q\", \"k\", \"v\"]], \"in_proj_bias\", \"bias_k\", \"bias_v\"]:\n tensor = getattr(l, attr)\n if tensor is not None:\n tensor.data = tensor.data.to(dtype)\n\n for name in [\"text_projection\", \"proj\"]:\n if hasattr(l, name):\n attr = getattr(l, name)\n if attr is not None:\n attr.data = attr.data.to(dtype)\n\n model.apply(_convert_weights)\n\n\nconvert_weights_to_fp16 = convert_weights_to_lp # backwards compat\n\n\n# used to maintain checkpoint compatibility\ndef convert_to_custom_text_state_dict(state_dict: dict):\n if 'text_projection' in state_dict:\n # old format state_dict, move text tower -> .text\n new_state_dict = {}\n for k, v in state_dict.items():\n if any(k.startswith(p) for p in (\n 'text_projection',\n 'positional_embedding',\n 'token_embedding',\n 'transformer',\n 'ln_final',\n )):\n k = 'text.' + k\n new_state_dict[k] = v\n return new_state_dict\n return state_dict\n\n\ndef build_model_from_openai_state_dict(\n state_dict: dict,\n quick_gelu=True,\n cast_dtype=torch.float16,\n):\n vit = \"visual.proj\" in state_dict\n\n if vit:\n vision_width = state_dict[\"visual.conv1.weight\"].shape[0]\n vision_layers = len(\n [k for k in state_dict.keys() if k.startswith(\"visual.\") and k.endswith(\".attn.in_proj_weight\")])\n vision_patch_size = state_dict[\"visual.conv1.weight\"].shape[-1]\n grid_size = round((state_dict[\"visual.positional_embedding\"].shape[0] - 1) ** 0.5)\n image_size = vision_patch_size * grid_size\n else:\n counts: list = [\n len(set(k.split(\".\")[2] for k in state_dict if k.startswith(f\"visual.layer{b}\"))) for b in [1, 2, 3, 4]]\n vision_layers = tuple(counts)\n vision_width = state_dict[\"visual.layer1.0.conv1.weight\"].shape[0]\n output_width = round((state_dict[\"visual.attnpool.positional_embedding\"].shape[0] - 1) ** 0.5)\n vision_patch_size = None\n assert output_width ** 2 + 1 == state_dict[\"visual.attnpool.positional_embedding\"].shape[0]\n image_size = output_width * 32\n\n embed_dim = state_dict[\"text_projection\"].shape[1]\n context_length = state_dict[\"positional_embedding\"].shape[0]\n vocab_size = state_dict[\"token_embedding.weight\"].shape[0]\n transformer_width = state_dict[\"ln_final.weight\"].shape[0]\n transformer_heads = transformer_width // 64\n transformer_layers = len(set(k.split(\".\")[2] for k in state_dict if k.startswith(f\"transformer.resblocks\")))\n\n vision_cfg = CLIPVisionCfg(\n layers=vision_layers,\n width=vision_width,\n patch_size=vision_patch_size,\n image_size=image_size,\n )\n text_cfg = CLIPTextCfg(\n context_length=context_length,\n vocab_size=vocab_size,\n width=transformer_width,\n heads=transformer_heads,\n layers=transformer_layers,\n )\n model = CLIP(\n embed_dim,\n vision_cfg=vision_cfg,\n text_cfg=text_cfg,\n quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU\n cast_dtype=cast_dtype,\n )\n\n for key in [\"input_resolution\", \"context_length\", \"vocab_size\"]:\n state_dict.pop(key, None)\n\n convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16\n model.load_state_dict(state_dict)\n return model.eval()\n\n\ndef trace_model(model, batch_size=256, device=torch.device('cpu')):\n model.eval()\n image_size = model.visual.image_size\n example_images = torch.ones((batch_size, 3, image_size, image_size), device=device)\n example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device)\n model = torch.jit.trace_module(\n model,\n inputs=dict(\n forward=(example_images, example_text),\n encode_text=(example_text,),\n encode_image=(example_images,)\n ))\n model.visual.image_size = image_size\n return model\n\n\ndef resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', antialias: bool = True):\n # Rescale the grid of position embeddings when loading from state_dict\n old_pos_embed = state_dict.get('visual.positional_embedding', None)\n if old_pos_embed is None or not hasattr(model.visual, 'grid_size'):\n return\n grid_size = to_2tuple(model.visual.grid_size)\n extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more)\n new_seq_len = grid_size[0] * grid_size[1] + extra_tokens\n if new_seq_len == old_pos_embed.shape[0]:\n return\n\n if extra_tokens:\n pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]\n else:\n pos_emb_tok, pos_emb_img = None, old_pos_embed\n old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))\n\n logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)\n pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)\n pos_emb_img = F.interpolate(\n pos_emb_img,\n size=grid_size,\n mode=interpolation,\n antialias=antialias,\n align_corners=False,\n )\n pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]\n if pos_emb_tok is not None:\n new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)\n else:\n new_pos_embed = pos_emb_img\n state_dict['visual.positional_embedding'] = new_pos_embed\n"
},
{
"path": "src/open_clip/model_configs/RN101-quickgelu.json",
"content": "{\n \"embed_dim\": 512,\n \"quick_gelu\": true,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": [\n 3,\n 4,\n 23,\n 3\n ],\n \"width\": 64,\n \"patch_size\": null\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/RN101.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": [\n 3,\n 4,\n 23,\n 3\n ],\n \"width\": 64,\n \"patch_size\": null\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/RN50-quickgelu.json",
"content": "{\n \"embed_dim\": 1024,\n \"quick_gelu\": true,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": [\n 3,\n 4,\n 6,\n 3\n ],\n \"width\": 64,\n \"patch_size\": null\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}\n"
},
{
"path": "src/open_clip/model_configs/RN50.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": [\n 3,\n 4,\n 6,\n 3\n ],\n \"width\": 64,\n \"patch_size\": null\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/RN50x16.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"image_size\": 384,\n \"layers\": [\n 6,\n 8,\n 18,\n 8\n ],\n \"width\": 96,\n \"patch_size\": null\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/RN50x4.json",
"content": "{\n \"embed_dim\": 640,\n \"vision_cfg\": {\n \"image_size\": 288,\n \"layers\": [\n 4,\n 6,\n 10,\n 6\n ],\n \"width\": 80,\n \"patch_size\": null\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 640,\n \"heads\": 10,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/RN50x64.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"image_size\": 448,\n \"layers\": [\n 3,\n 15,\n 36,\n 10\n ],\n \"width\": 128,\n \"patch_size\": null\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1024,\n \"heads\": 16,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-B-16-plus-240.json",
"content": "{\n \"embed_dim\": 640,\n \"vision_cfg\": {\n \"image_size\": 240,\n \"layers\": 12,\n \"width\": 896,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 640,\n \"heads\": 10,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-B-16-plus.json",
"content": "{\n \"embed_dim\": 640,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 896,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 640,\n \"heads\": 10,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-B-16.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-B-32-plus-256.json",
"content": "{\n \"embed_dim\": 640,\n \"vision_cfg\": {\n \"image_size\": 256,\n \"layers\": 12,\n \"width\": 896,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 640,\n \"heads\": 10,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-B-32-quickgelu.json",
"content": "{\n \"embed_dim\": 512,\n \"quick_gelu\": true,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-B-32.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-H-14.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 32,\n \"width\": 1280,\n \"head_width\": 80,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1024,\n \"heads\": 16,\n \"layers\": 24\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-H-16.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 32,\n \"width\": 1280,\n \"head_width\": 80,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1024,\n \"heads\": 16,\n \"layers\": 24\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-L-14-280.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"image_size\": 280,\n \"layers\": 24,\n \"width\": 1024,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-L-14-336.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"image_size\": 336,\n \"layers\": 24,\n \"width\": 1024,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-L-14.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 24,\n \"width\": 1024,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-L-16-320.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"image_size\": 320,\n \"layers\": 24,\n \"width\": 1024,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-L-16.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 24,\n \"width\": 1024,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-M-16-alt.json",
"content": "{\n \"embed_dim\": 384,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 512,\n \"patch_size\": 16,\n \"ls_init_value\": 1e-4\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 384,\n \"heads\": 6,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-M-16.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 512,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-M-32-alt.json",
"content": "{\n \"embed_dim\": 384,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 512,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 384,\n \"heads\": 6,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-M-32.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 512,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-S-16-alt.json",
"content": "{\n \"embed_dim\": 256,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 384,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 256,\n \"heads\": 4,\n \"layers\": 10\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-S-16.json",
"content": "{\n \"embed_dim\": 384,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 384,\n \"patch_size\": 16\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 384,\n \"heads\": 6,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-S-32-alt.json",
"content": "{\n \"embed_dim\": 256,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 384,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 256,\n \"heads\": 4,\n \"layers\": 10\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-S-32.json",
"content": "{\n \"embed_dim\": 384,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 384,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 384,\n \"heads\": 6,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-bigG-14.json",
"content": "{\n \"embed_dim\": 1280,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 48,\n \"width\": 1664,\n \"head_width\": 104,\n \"mlp_ratio\": 4.9231,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1280,\n \"heads\": 20,\n \"layers\": 32\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-e-14.json",
"content": "{\n \"embed_dim\": 1280,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 56,\n \"width\": 1792,\n \"head_width\": 112,\n \"mlp_ratio\": 8.5715,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1280,\n \"heads\": 20,\n \"layers\": 36\n }\n}"
},
{
"path": "src/open_clip/model_configs/ViT-g-14.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 40,\n \"width\": 1408,\n \"head_width\": 88,\n \"mlp_ratio\": 4.3637,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1024,\n \"heads\": 16,\n \"layers\": 24\n }\n}"
},
{
"path": "src/open_clip/model_configs/coca_ViT-B-32.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 32,\n \"attentional_pool\": true,\n \"attn_pooler_heads\": 8,\n \"output_tokens\": true\n },\n \"text_cfg\": {\n \"context_length\": 76,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12,\n \"embed_cls\": true,\n \"output_tokens\": true\n },\n \"multimodal_cfg\": {\n \"context_length\": 76,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12,\n \"attn_pooler_heads\": 8\n },\n \"custom_text\": true\n}"
},
{
"path": "src/open_clip/model_configs/coca_ViT-L-14.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 24,\n \"width\": 1024,\n \"patch_size\": 14,\n \"attentional_pool\": true,\n \"attn_pooler_heads\": 8,\n \"output_tokens\": true\n },\n \"text_cfg\": {\n \"context_length\": 76,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12,\n \"embed_cls\": true,\n \"output_tokens\": true\n },\n \"multimodal_cfg\": {\n \"context_length\": 76,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12,\n \"attn_pooler_heads\": 12\n },\n \"custom_text\": true\n}\n"
},
{
"path": "src/open_clip/model_configs/coca_base.json",
"content": "{\n \"embed_dim\": 512,\n \"multimodal_cfg\": {\n \"width\": 768,\n \"context_length\": 76,\n \"vocab_size\": 64000,\n \"mlp_ratio\": 4,\n \"layers\": 12,\n \"dim_head\": 64,\n \"heads\": 12,\n \"n_queries\": 256,\n \"attn_pooler_heads\": 8\n },\n \"vision_cfg\": {\n \"image_size\": 288,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 18,\n \"output_tokens\": true\n },\n \"text_cfg\": {\n \"context_length\": 76,\n \"vocab_size\": 64000,\n \"layers\": 12,\n \"heads\": 12,\n \"width\": 768,\n \"embed_cls\": true,\n \"output_tokens\": true\n },\n \"custom_text\": true\n}"
},
{
"path": "src/open_clip/model_configs/coca_roberta-ViT-B-32.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 32,\n \"output_tokens\": true\n },\n \"text_cfg\": {\n \"hf_model_name\": \"roberta-base\",\n \"hf_tokenizer_name\": \"roberta-base\",\n \"proj\": \"linear\",\n \"width\": 768,\n \"output_tokens\": true\n },\n \"multimodal_cfg\": {\n \"context_length\": 76,\n \"width\": 768,\n \"heads\": 8,\n \"layers\": 12\n },\n \"custom_text\": true\n}\n"
},
{
"path": "src/open_clip/model_configs/convnext_base.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_base\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 224\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_base_w.json",
"content": "{\n \"embed_dim\": 640,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_base\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 256\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 640,\n \"heads\": 10,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_base_w_320.json",
"content": "{\n \"embed_dim\": 640,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_base\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 320\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 640,\n \"heads\": 10,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_large.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_large\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 224\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_large_d.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_large\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"mlp\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 256\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 16\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_large_d_320.json",
"content": "{\n \"embed_dim\": 768,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_large\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"mlp\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 320\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 768,\n \"heads\": 12,\n \"layers\": 16\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_small.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_small\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 224\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_tiny.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_tiny\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 224\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_xlarge.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_xlarge\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 256\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1024,\n \"heads\": 16,\n \"layers\": 20\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_xxlarge.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_xxlarge\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 256\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1024,\n \"heads\": 16,\n \"layers\": 24\n }\n}"
},
{
"path": "src/open_clip/model_configs/convnext_xxlarge_320.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"timm_model_name\": \"convnext_xxlarge\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"timm_drop\": 0.0,\n \"timm_drop_path\": 0.1,\n \"image_size\": 320\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 1024,\n \"heads\": 16,\n \"layers\": 24\n }\n}"
},
{
"path": "src/open_clip/model_configs/mt5-base-ViT-B-32.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"hf_model_name\": \"google/mt5-base\",\n \"hf_tokenizer_name\": \"google/mt5-base\",\n \"proj\": \"mlp\",\n \"pooler_type\": \"mean_pooler\"\n }\n}\n"
},
{
"path": "src/open_clip/model_configs/mt5-xl-ViT-H-14.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 32,\n \"width\": 1280,\n \"head_width\": 80,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"hf_model_name\": \"google/mt5-xl\",\n \"hf_tokenizer_name\": \"google/mt5-xl\",\n \"proj\": \"mlp\",\n \"pooler_type\": \"mean_pooler\"\n }\n}\n"
},
{
"path": "src/open_clip/model_configs/roberta-ViT-B-32.json",
"content": "{\n \"embed_dim\": 512,\n \"quick_gelu\": true,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"hf_model_name\": \"roberta-base\",\n \"hf_tokenizer_name\": \"roberta-base\",\n \"proj\": \"mlp\",\n \"pooler_type\": \"mean_pooler\"\n }\n}\n"
},
{
"path": "src/open_clip/model_configs/swin_base_patch4_window7_224.json",
"content": "{\n \"embed_dim\": 640,\n \"vision_cfg\": {\n \"timm_model_name\": \"swin_base_patch4_window7_224\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"image_size\": 224\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 640,\n \"heads\": 10,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/vit_medium_patch16_gap_256.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"timm_model_name\": \"vit_medium_patch16_gap_256\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"image_size\": 256\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/vit_relpos_medium_patch16_cls_224.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"timm_model_name\": \"vit_relpos_medium_patch16_cls_224\",\n \"timm_model_pretrained\": false,\n \"timm_pool\": \"\",\n \"timm_proj\": \"linear\",\n \"image_size\": 224\n },\n \"text_cfg\": {\n \"context_length\": 77,\n \"vocab_size\": 49408,\n \"width\": 512,\n \"heads\": 8,\n \"layers\": 12\n }\n}"
},
{
"path": "src/open_clip/model_configs/xlm-roberta-base-ViT-B-32.json",
"content": "{\n \"embed_dim\": 512,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 12,\n \"width\": 768,\n \"patch_size\": 32\n },\n \"text_cfg\": {\n \"hf_model_name\": \"xlm-roberta-base\",\n \"hf_tokenizer_name\": \"xlm-roberta-base\",\n \"proj\": \"mlp\",\n \"pooler_type\": \"mean_pooler\"\n }\n}\n"
},
{
"path": "src/open_clip/model_configs/xlm-roberta-large-ViT-H-14.json",
"content": "{\n \"embed_dim\": 1024,\n \"vision_cfg\": {\n \"image_size\": 224,\n \"layers\": 32,\n \"width\": 1280,\n \"head_width\": 80,\n \"patch_size\": 14\n },\n \"text_cfg\": {\n \"hf_model_name\": \"xlm-roberta-large\",\n \"hf_tokenizer_name\": \"xlm-roberta-large\",\n \"proj\": \"mlp\",\n \"pooler_type\": \"mean_pooler\"\n }\n}\n"
},
{
"path": "src/open_clip/modified_resnet.py",
"content": "from collections import OrderedDict\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .utils import freeze_batch_norm_2d\n\n\nclass Bottleneck(nn.Module):\n expansion = 4\n\n def __init__(self, inplanes, planes, stride=1):\n super().__init__()\n\n # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1\n self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.act1 = nn.ReLU(inplace=True)\n\n self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.act2 = nn.ReLU(inplace=True)\n\n self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()\n\n self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)\n self.bn3 = nn.BatchNorm2d(planes * self.expansion)\n self.act3 = nn.ReLU(inplace=True)\n\n self.downsample = None\n self.stride = stride\n\n if stride > 1 or inplanes != planes * Bottleneck.expansion:\n # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1\n self.downsample = nn.Sequential(OrderedDict([\n (\"-1\", nn.AvgPool2d(stride)),\n (\"0\", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),\n (\"1\", nn.BatchNorm2d(planes * self.expansion))\n ]))\n\n def forward(self, x: torch.Tensor):\n identity = x\n\n out = self.act1(self.bn1(self.conv1(x)))\n out = self.act2(self.bn2(self.conv2(out)))\n out = self.avgpool(out)\n out = self.bn3(self.conv3(out))\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out += identity\n out = self.act3(out)\n return out\n\n\nclass AttentionPool2d(nn.Module):\n def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):\n super().__init__()\n self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)\n self.k_proj = nn.Linear(embed_dim, embed_dim)\n self.q_proj = nn.Linear(embed_dim, embed_dim)\n self.v_proj = nn.Linear(embed_dim, embed_dim)\n self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)\n self.num_heads = num_heads\n\n def forward(self, x):\n x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC\n x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC\n x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC\n x, _ = F.multi_head_attention_forward(\n query=x, key=x, value=x,\n embed_dim_to_check=x.shape[-1],\n num_heads=self.num_heads,\n q_proj_weight=self.q_proj.weight,\n k_proj_weight=self.k_proj.weight,\n v_proj_weight=self.v_proj.weight,\n in_proj_weight=None,\n in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),\n bias_k=None,\n bias_v=None,\n add_zero_attn=False,\n dropout_p=0.,\n out_proj_weight=self.c_proj.weight,\n out_proj_bias=self.c_proj.bias,\n use_separate_proj_weight=True,\n training=self.training,\n need_weights=False\n )\n\n return x[0]\n\n\nclass ModifiedResNet(nn.Module):\n \"\"\"\n A ResNet class that is similar to torchvision's but contains the following changes:\n - There are now 3 \"stem\" convolutions as opposed to 1, with an average pool instead of a max pool.\n - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1\n - The final pooling layer is a QKV attention instead of an average pool\n \"\"\"\n\n def __init__(self, layers, output_dim, heads, image_size=224, width=64):\n super().__init__()\n self.output_dim = output_dim\n self.image_size = image_size\n\n # the 3-layer stem\n self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(width // 2)\n self.act1 = nn.ReLU(inplace=True)\n self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(width // 2)\n self.act2 = nn.ReLU(inplace=True)\n self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)\n self.bn3 = nn.BatchNorm2d(width)\n self.act3 = nn.ReLU(inplace=True)\n self.avgpool = nn.AvgPool2d(2)\n\n # residual layers\n self._inplanes = width # this is a *mutable* variable used during construction\n self.layer1 = self._make_layer(width, layers[0])\n self.layer2 = self._make_layer(width * 2, layers[1], stride=2)\n self.layer3 = self._make_layer(width * 4, layers[2], stride=2)\n self.layer4 = self._make_layer(width * 8, layers[3], stride=2)\n\n embed_dim = width * 32 # the ResNet feature dimension\n self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)\n\n self.init_parameters()\n\n def _make_layer(self, planes, blocks, stride=1):\n layers = [Bottleneck(self._inplanes, planes, stride)]\n\n self._inplanes = planes * Bottleneck.expansion\n for _ in range(1, blocks):\n layers.append(Bottleneck(self._inplanes, planes))\n\n return nn.Sequential(*layers)\n\n def init_parameters(self):\n if self.attnpool is not None:\n std = self.attnpool.c_proj.in_features ** -0.5\n nn.init.normal_(self.attnpool.q_proj.weight, std=std)\n nn.init.normal_(self.attnpool.k_proj.weight, std=std)\n nn.init.normal_(self.attnpool.v_proj.weight, std=std)\n nn.init.normal_(self.attnpool.c_proj.weight, std=std)\n\n for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:\n for name, param in resnet_block.named_parameters():\n if name.endswith(\"bn3.weight\"):\n nn.init.zeros_(param)\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n assert unlocked_groups == 0, 'partial locking not currently supported for this model'\n for param in self.parameters():\n param.requires_grad = False\n if freeze_bn_stats:\n freeze_batch_norm_2d(self)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n # FIXME support for non-transformer\n pass\n\n def stem(self, x):\n x = self.act1(self.bn1(self.conv1(x)))\n x = self.act2(self.bn2(self.conv2(x)))\n x = self.act3(self.bn3(self.conv3(x)))\n x = self.avgpool(x)\n return x\n\n def forward(self, x):\n x = self.stem(x)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n x = self.attnpool(x)\n\n return x\n"
},
{
"path": "src/open_clip/openai.py",
"content": "\"\"\" OpenAI pretrained model functions\n\nAdapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.\n\"\"\"\n\nimport os\nimport warnings\nfrom typing import List, Optional, Union\n\nimport torch\n\nfrom .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype\nfrom .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url\n\n__all__ = [\"list_openai_models\", \"load_openai_model\"]\n\n\ndef list_openai_models() -> List[str]:\n \"\"\"Returns the names of available CLIP models\"\"\"\n return list_pretrained_models_by_tag('openai')\n\n\ndef load_openai_model(\n name: str,\n precision: Optional[str] = None,\n device: Optional[Union[str, torch.device]] = None,\n jit: bool = True,\n cache_dir: Optional[str] = None,\n):\n \"\"\"Load a CLIP model\n\n Parameters\n ----------\n name : str\n A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict\n precision: str\n Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'.\n device : Union[str, torch.device]\n The device to put the loaded model\n jit : bool\n Whether to load the optimized JIT model (default) or more hackable non-JIT model.\n cache_dir : Optional[str]\n The directory to cache the downloaded model weights\n\n Returns\n -------\n model : torch.nn.Module\n The CLIP model\n preprocess : Callable[[PIL.Image], torch.Tensor]\n A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input\n \"\"\"\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n if precision is None:\n precision = 'fp32' if device == 'cpu' else 'fp16'\n\n if get_pretrained_url(name, 'openai'):\n model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir)\n elif os.path.isfile(name):\n model_path = name\n else:\n raise RuntimeError(f\"Model {name} not found; available models = {list_openai_models()}\")\n\n try:\n # loading JIT archive\n model = torch.jit.load(model_path, map_location=device if jit else \"cpu\").eval()\n state_dict = None\n except RuntimeError:\n # loading saved state dict\n if jit:\n warnings.warn(f\"File {model_path} is not a JIT archive. Loading as a state dict instead\")\n jit = False\n state_dict = torch.load(model_path, map_location=\"cpu\")\n\n if not jit:\n # Build a non-jit model from the OpenAI jitted model state dict\n cast_dtype = get_cast_dtype(precision)\n try:\n model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype)\n except KeyError:\n sd = {k[7:]: v for k, v in state_dict[\"state_dict\"].items()}\n model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype)\n\n # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use\n model = model.to(device)\n if precision.startswith('amp') or precision == 'fp32':\n model.float()\n elif precision == 'bf16':\n convert_weights_to_lp(model, dtype=torch.bfloat16)\n\n return model\n\n # patch the device names\n device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])\n device_node = [n for n in device_holder.graph.findAllNodes(\"prim::Constant\") if \"Device\" in repr(n)][-1]\n\n def patch_device(module):\n try:\n graphs = [module.graph] if hasattr(module, \"graph\") else []\n except RuntimeError:\n graphs = []\n\n if hasattr(module, \"forward1\"):\n graphs.append(module.forward1.graph)\n\n for graph in graphs:\n for node in graph.findAllNodes(\"prim::Constant\"):\n if \"value\" in node.attributeNames() and str(node[\"value\"]).startswith(\"cuda\"):\n node.copyAttributes(device_node)\n\n model.apply(patch_device)\n patch_device(model.encode_image)\n patch_device(model.encode_text)\n\n # patch dtype to float32 (typically for CPU)\n if precision == 'fp32':\n float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])\n float_input = list(float_holder.graph.findNode(\"aten::to\").inputs())[1]\n float_node = float_input.node()\n\n def patch_float(module):\n try:\n graphs = [module.graph] if hasattr(module, \"graph\") else []\n except RuntimeError:\n graphs = []\n\n if hasattr(module, \"forward1\"):\n graphs.append(module.forward1.graph)\n\n for graph in graphs:\n for node in graph.findAllNodes(\"aten::to\"):\n inputs = list(node.inputs())\n for i in [1, 2]: # dtype can be the second or third argument to aten::to()\n if inputs[i].node()[\"value\"] == 5:\n inputs[i].node().copyAttributes(float_node)\n\n model.apply(patch_float)\n patch_float(model.encode_image)\n patch_float(model.encode_text)\n model.float()\n\n # ensure image_size attr available at consistent location for both jit and non-jit\n model.visual.image_size = model.input_resolution.item()\n return model\n"
},
{
"path": "src/open_clip/pretrained.py",
"content": "import hashlib\nimport os\nimport urllib\nimport warnings\nfrom functools import partial\nfrom typing import Dict, Union\n\nfrom tqdm import tqdm\n\nfrom .version import __version__\n\ntry:\n from huggingface_hub import hf_hub_download\n hf_hub_download = partial(hf_hub_download, library_name=\"open_clip\", library_version=__version__)\n _has_hf_hub = True\nexcept ImportError:\n hf_hub_download = None\n _has_hf_hub = False\n\n\ndef _pcfg(url='', hf_hub='', mean=None, std=None):\n return dict(\n url=url,\n hf_hub=hf_hub,\n mean=mean,\n std=std,\n )\n\n\n_RN50 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt\"),\n yfcc15m=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt\"),\n cc12m=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt\"),\n)\n\n_RN50_quickgelu = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt\"),\n yfcc15m=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt\"),\n cc12m=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt\"),\n)\n\n_RN101 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt\"),\n yfcc15m=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt\"),\n)\n\n_RN101_quickgelu = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt\"),\n yfcc15m=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt\"),\n)\n\n_RN50x4 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt\"),\n)\n\n_RN50x16 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt\"),\n)\n\n_RN50x64 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt\"),\n)\n\n_VITB32 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt\"),\n laion400m_e31=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt\"),\n laion400m_e32=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt\"),\n laion2b_e16=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth\"),\n laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/')\n)\n\n_VITB32_quickgelu = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt\"),\n laion400m_e31=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt\"),\n laion400m_e32=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt\"),\n)\n\n_VITB16 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt\"),\n laion400m_e31=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt\"),\n laion400m_e32=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt\"),\n # laion400m_32k=_pcfg(\n # url=\"\",\n # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n # laion400m_64k=_pcfg(\n # url=\"\",\n # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'),\n)\n\n_VITB16_PLUS_240 = dict(\n laion400m_e31=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt\"),\n laion400m_e32=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt\"),\n)\n\n_VITL14 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt\"),\n laion400m_e31=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt\"),\n laion400m_e32=_pcfg(\n \"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt\"),\n laion2b_s32b_b82k=_pcfg(\n hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/',\n mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),\n)\n\n_VITL14_336 = dict(\n openai=_pcfg(\n \"https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt\"),\n)\n\n_VITH14 = dict(\n laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'),\n)\n\n_VITg14 = dict(\n laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'),\n laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'),\n)\n\n_VITbigG14 = dict(\n laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'),\n)\n\n_robertaViTB32 = dict(\n laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'),\n)\n\n_xlmRobertaBaseViTB32 = dict(\n laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'),\n)\n\n_xlmRobertaLargeFrozenViTH14 = dict(\n frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'),\n)\n\n_convnext_base = dict(\n laion400m_s13b_b51k=_pcfg(hf_hub='laion/CLIP-convnext_base-laion400M-s13B-b51K/'),\n)\n\n_convnext_base_w = dict(\n laion2b_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K/'),\n laion2b_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/'),\n laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/'),\n)\n\n_convnext_base_w_320 = dict(\n laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/'),\n laion_aesthetic_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/'),\n)\n\n_convnext_large_d = dict(\n laion2b_s26b_b102k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg/'),\n)\n\n_convnext_large_d_320 = dict(\n laion2b_s29b_b131k_ft=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft/'),\n laion2b_s29b_b131k_ft_soup=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup/'),\n)\n\n_convnext_xxlarge = dict(\n laion2b_s34b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/'),\n laion2b_s34b_b82k_augreg_rewind=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind/'),\n laion2b_s34b_b82k_augreg_soup=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup/'),\n)\n\n_coca_VITB32 = dict(\n laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-B-32-laion2B-s13B-b90k/'),\n mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-B-32-laion2B-s13B-b90k/')\n)\n\n_coca_VITL14 = dict(\n laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-L-14-laion2B-s13B-b90k/'),\n mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-L-14-laion2B-s13B-b90k/')\n)\n\n\n_PRETRAINED = {\n \"RN50\": _RN50,\n \"RN50-quickgelu\": _RN50_quickgelu,\n \"RN101\": _RN101,\n \"RN101-quickgelu\": _RN101_quickgelu,\n \"RN50x4\": _RN50x4,\n \"RN50x16\": _RN50x16,\n \"RN50x64\": _RN50x64,\n \"ViT-B-32\": _VITB32,\n \"ViT-B-32-quickgelu\": _VITB32_quickgelu,\n \"ViT-B-16\": _VITB16,\n \"ViT-B-16-plus-240\": _VITB16_PLUS_240,\n \"ViT-L-14\": _VITL14,\n \"ViT-L-14-336\": _VITL14_336,\n \"ViT-H-14\": _VITH14,\n \"ViT-g-14\": _VITg14,\n \"ViT-bigG-14\": _VITbigG14,\n \"roberta-ViT-B-32\": _robertaViTB32,\n \"xlm-roberta-base-ViT-B-32\": _xlmRobertaBaseViTB32,\n \"xlm-roberta-large-ViT-H-14\": _xlmRobertaLargeFrozenViTH14,\n \"convnext_base\": _convnext_base,\n \"convnext_base_w\": _convnext_base_w,\n \"convnext_base_w_320\": _convnext_base_w_320,\n \"convnext_large_d\": _convnext_large_d,\n \"convnext_large_d_320\": _convnext_large_d_320,\n \"convnext_xxlarge\": _convnext_xxlarge,\n \"coca_ViT-B-32\": _coca_VITB32,\n \"coca_ViT-L-14\": _coca_VITL14,\n}\n\n\ndef _clean_tag(tag: str):\n # normalize pretrained tags\n return tag.lower().replace('-', '_')\n\n\ndef list_pretrained(as_str: bool = False):\n \"\"\" returns list of pretrained models\n Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True\n \"\"\"\n return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()]\n\n\ndef list_pretrained_models_by_tag(tag: str):\n \"\"\" return all models having the specified pretrain tag \"\"\"\n models = []\n tag = _clean_tag(tag)\n for k in _PRETRAINED.keys():\n if tag in _PRETRAINED[k]:\n models.append(k)\n return models\n\n\ndef list_pretrained_tags_by_model(model: str):\n \"\"\" return all pretrain tags for the specified model architecture \"\"\"\n tags = []\n if model in _PRETRAINED:\n tags.extend(_PRETRAINED[model].keys())\n return tags\n\n\ndef is_pretrained_cfg(model: str, tag: str):\n if model not in _PRETRAINED:\n return False\n return _clean_tag(tag) in _PRETRAINED[model]\n\n\ndef get_pretrained_cfg(model: str, tag: str):\n if model not in _PRETRAINED:\n return {}\n model_pretrained = _PRETRAINED[model]\n return model_pretrained.get(_clean_tag(tag), {})\n\n\ndef get_pretrained_url(model: str, tag: str):\n cfg = get_pretrained_cfg(model, _clean_tag(tag))\n return cfg.get('url', '')\n\n\ndef download_pretrained_from_url(\n url: str,\n cache_dir: Union[str, None] = None,\n):\n if not cache_dir:\n cache_dir = os.path.expanduser(\"~/.cache/clip\")\n os.makedirs(cache_dir, exist_ok=True)\n filename = os.path.basename(url)\n\n if 'openaipublic' in url:\n expected_sha256 = url.split(\"/\")[-2]\n elif 'mlfoundations' in url:\n expected_sha256 = os.path.splitext(filename)[0].split(\"-\")[-1]\n else:\n expected_sha256 = ''\n\n download_target = os.path.join(cache_dir, filename)\n\n if os.path.exists(download_target) and not os.path.isfile(download_target):\n raise RuntimeError(f\"{download_target} exists and is not a regular file\")\n\n if os.path.isfile(download_target):\n if expected_sha256:\n if hashlib.sha256(open(download_target, \"rb\").read()).hexdigest().startswith(expected_sha256):\n return download_target\n else:\n warnings.warn(f\"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file\")\n else:\n return download_target\n\n with urllib.request.urlopen(url) as source, open(download_target, \"wb\") as output:\n with tqdm(total=int(source.headers.get(\"Content-Length\")), ncols=80, unit='iB', unit_scale=True) as loop:\n while True:\n buffer = source.read(8192)\n if not buffer:\n break\n\n output.write(buffer)\n loop.update(len(buffer))\n\n if expected_sha256 and not hashlib.sha256(open(download_target, \"rb\").read()).hexdigest().startswith(expected_sha256):\n raise RuntimeError(f\"Model has been downloaded but the SHA256 checksum does not not match\")\n\n return download_target\n\n\ndef has_hf_hub(necessary=False):\n if not _has_hf_hub and necessary:\n # if no HF Hub module installed, and it is necessary to continue, raise error\n raise RuntimeError(\n 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')\n return _has_hf_hub\n\n\ndef download_pretrained_from_hf(\n model_id: str,\n filename: str = 'open_clip_pytorch_model.bin',\n revision=None,\n cache_dir: Union[str, None] = None,\n):\n has_hf_hub(True)\n cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir)\n return cached_file\n\n\ndef download_pretrained(\n cfg: Dict,\n force_hf_hub: bool = False,\n cache_dir: Union[str, None] = None,\n):\n target = ''\n if not cfg:\n return target\n\n download_url = cfg.get('url', '')\n download_hf_hub = cfg.get('hf_hub', '')\n if download_hf_hub and force_hf_hub:\n # use HF hub even if url exists\n download_url = ''\n\n if download_url:\n target = download_pretrained_from_url(download_url, cache_dir=cache_dir)\n elif download_hf_hub:\n has_hf_hub(True)\n # we assume the hf_hub entries in pretrained config combine model_id + filename in\n # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and\n # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'.\n model_id, filename = os.path.split(download_hf_hub)\n if filename:\n target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir)\n else:\n target = download_pretrained_from_hf(model_id, cache_dir=cache_dir)\n\n return target\n"
},
{
"path": "src/open_clip/push_to_hf_hub.py",
"content": "import argparse\nimport json\nfrom pathlib import Path\nfrom tempfile import TemporaryDirectory\nfrom typing import Optional, Tuple\n\nimport torch\n\ntry:\n from huggingface_hub import (\n create_repo,\n get_hf_file_metadata,\n hf_hub_download,\n hf_hub_url,\n repo_type_and_id_from_hf_id,\n upload_folder,\n )\n from huggingface_hub.utils import EntryNotFoundError\n _has_hf_hub = True\nexcept ImportError:\n _has_hf_hub = False\n\nfrom .factory import create_model_from_pretrained, get_model_config, get_tokenizer\nfrom .tokenizer import HFTokenizer\n\n\ndef save_config_for_hf(\n model,\n config_path: str,\n model_config: Optional[dict]\n):\n preprocess_cfg = {\n 'mean': model.visual.image_mean,\n 'std': model.visual.image_std,\n }\n hf_config = {\n 'model_cfg': model_config,\n 'preprocess_cfg': preprocess_cfg,\n }\n\n with config_path.open('w') as f:\n json.dump(hf_config, f, indent=2)\n\n\ndef save_for_hf(\n model,\n tokenizer: HFTokenizer,\n model_config: dict,\n save_directory: str,\n weights_filename='open_clip_pytorch_model.bin',\n config_filename='open_clip_config.json',\n):\n save_directory = Path(save_directory)\n save_directory.mkdir(exist_ok=True, parents=True)\n\n weights_path = save_directory / weights_filename\n torch.save(model.state_dict(), weights_path)\n\n tokenizer.save_pretrained(save_directory)\n\n config_path = save_directory / config_filename\n save_config_for_hf(model, config_path, model_config=model_config)\n\n\ndef push_to_hf_hub(\n model,\n tokenizer,\n model_config: Optional[dict],\n repo_id: str,\n commit_message: str = 'Add model',\n token: Optional[str] = None,\n revision: Optional[str] = None,\n private: bool = False,\n create_pr: bool = False,\n model_card: Optional[dict] = None,\n):\n if not isinstance(tokenizer, HFTokenizer):\n # default CLIP tokenizers use https://huggingface.co/openai/clip-vit-large-patch14\n tokenizer = HFTokenizer('openai/clip-vit-large-patch14')\n\n # Create repo if it doesn't exist yet\n repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)\n\n # Infer complete repo_id from repo_url\n # Can be different from the input `repo_id` if repo_owner was implicit\n _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)\n repo_id = f\"{repo_owner}/{repo_name}\"\n\n # Check if README file already exist in repo\n try:\n get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename=\"README.md\", revision=revision))\n has_readme = True\n except EntryNotFoundError:\n has_readme = False\n\n # Dump model and push to Hub\n with TemporaryDirectory() as tmpdir:\n # Save model weights and config.\n save_for_hf(\n model,\n tokenizer=tokenizer,\n model_config=model_config,\n save_directory=tmpdir,\n )\n\n # Add readme if it does not exist\n if not has_readme:\n model_card = model_card or {}\n model_name = repo_id.split('/')[-1]\n readme_path = Path(tmpdir) / \"README.md\"\n readme_text = generate_readme(model_card, model_name)\n readme_path.write_text(readme_text)\n\n # Upload model and return\n return upload_folder(\n repo_id=repo_id,\n folder_path=tmpdir,\n revision=revision,\n create_pr=create_pr,\n commit_message=commit_message,\n )\n\n\ndef push_pretrained_to_hf_hub(\n model_name,\n pretrained: str,\n repo_id: str,\n image_mean: Optional[Tuple[float, ...]] = None,\n image_std: Optional[Tuple[float, ...]] = None,\n commit_message: str = 'Add model',\n token: Optional[str] = None,\n revision: Optional[str] = None,\n private: bool = False,\n create_pr: bool = False,\n model_card: Optional[dict] = None,\n):\n model, preprocess_eval = create_model_from_pretrained(\n model_name,\n pretrained=pretrained,\n image_mean=image_mean,\n image_std=image_std,\n )\n\n model_config = get_model_config(model_name)\n assert model_config\n\n tokenizer = get_tokenizer(model_name)\n\n push_to_hf_hub(\n model=model,\n tokenizer=tokenizer,\n model_config=model_config,\n repo_id=repo_id,\n commit_message=commit_message,\n token=token,\n revision=revision,\n private=private,\n create_pr=create_pr,\n model_card=model_card,\n )\n\n\ndef generate_readme(model_card: dict, model_name: str):\n readme_text = \"---\\n\"\n readme_text += \"tags:\\n- zero-shot-image-classification\\n- clip\\n\"\n readme_text += \"library_tag: open_clip\\n\"\n readme_text += f\"license: {model_card.get('license', 'mit')}\\n\"\n if 'details' in model_card and 'Dataset' in model_card['details']:\n readme_text += 'datasets:\\n'\n readme_text += f\"- {model_card['details']['Dataset'].lower()}\\n\"\n readme_text += \"---\\n\"\n readme_text += f\"# Model card for {model_name}\\n\"\n if 'description' in model_card:\n readme_text += f\"\\n{model_card['description']}\\n\"\n if 'details' in model_card:\n readme_text += f\"\\n## Model Details\\n\"\n for k, v in model_card['details'].items():\n if isinstance(v, (list, tuple)):\n readme_text += f\"- **{k}:**\\n\"\n for vi in v:\n readme_text += f\" - {vi}\\n\"\n elif isinstance(v, dict):\n readme_text += f\"- **{k}:**\\n\"\n for ki, vi in v.items():\n readme_text += f\" - {ki}: {vi}\\n\"\n else:\n readme_text += f\"- **{k}:** {v}\\n\"\n if 'usage' in model_card:\n readme_text += f\"\\n## Model Usage\\n\"\n readme_text += model_card['usage']\n readme_text += '\\n'\n\n if 'comparison' in model_card:\n readme_text += f\"\\n## Model Comparison\\n\"\n readme_text += model_card['comparison']\n readme_text += '\\n'\n\n if 'citation' in model_card:\n readme_text += f\"\\n## Citation\\n\"\n if not isinstance(model_card['citation'], (list, tuple)):\n citations = [model_card['citation']]\n else:\n citations = model_card['citation']\n for c in citations:\n readme_text += f\"```bibtex\\n{c}\\n```\\n\"\n\n return readme_text\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Push to Hugging Face Hub\")\n parser.add_argument(\n \"--model\", type=str, help=\"Name of the model to use.\",\n )\n parser.add_argument(\n \"--pretrained\", type=str,\n help=\"Use a pretrained CLIP model weights with the specified tag or file path.\",\n )\n parser.add_argument(\n \"--repo-id\", type=str,\n help=\"Destination HF Hub repo-id ie 'organization/model_id'.\",\n )\n parser.add_argument(\n '--image-mean', type=float, nargs='+', default=None, metavar='MEAN',\n help='Override default image mean value of dataset')\n parser.add_argument(\n '--image-std', type=float, nargs='+', default=None, metavar='STD',\n help='Override default image std deviation of of dataset')\n args = parser.parse_args()\n\n print(f'Saving model {args.model} with pretrained weights {args.pretrained} to Hugging Face Hub at {args.repo_id}')\n\n # FIXME add support to pass model_card json / template from file via cmd line\n\n push_pretrained_to_hf_hub(\n args.model,\n args.pretrained,\n args.repo_id,\n image_mean=args.image_mean, # override image mean/std if trained w/ non defaults\n image_std=args.image_std,\n )\n\n print(f'{args.model} saved.')\n"
},
{
"path": "src/open_clip/timm_model.py",
"content": "\"\"\" timm model adapter\n\nWraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model.\n\"\"\"\nimport logging\nfrom collections import OrderedDict\n\nimport torch\nimport torch.nn as nn\n\ntry:\n import timm\n from timm.models.layers import Mlp, to_2tuple\n try:\n # old timm imports < 0.8.1\n from timm.models.layers.attention_pool2d import RotAttentionPool2d\n from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d\n except ImportError:\n # new timm imports >= 0.8.1\n from timm.layers import RotAttentionPool2d\n from timm.layers import AttentionPool2d as AbsAttentionPool2d\nexcept ImportError:\n timm = None\n\nfrom .utils import freeze_batch_norm_2d\n\n\nclass TimmModel(nn.Module):\n \"\"\" timm model adapter\n # FIXME this adapter is a work in progress, may change in ways that break weight compat\n \"\"\"\n\n def __init__(\n self,\n model_name,\n embed_dim,\n image_size=224,\n pool='avg',\n proj='linear',\n proj_bias=False,\n drop=0.,\n drop_path=None,\n pretrained=False,\n ):\n super().__init__()\n if timm is None:\n raise RuntimeError(\"Please `pip install timm` to use timm models.\")\n\n self.image_size = to_2tuple(image_size)\n timm_kwargs = {}\n if drop_path is not None:\n timm_kwargs['drop_path_rate'] = drop_path\n self.trunk = timm.create_model(model_name, pretrained=pretrained, **timm_kwargs)\n feat_size = self.trunk.default_cfg.get('pool_size', None)\n feature_ndim = 1 if not feat_size else 2\n if pool in ('abs_attn', 'rot_attn'):\n assert feature_ndim == 2\n # if attn pooling used, remove both classifier and default pool\n self.trunk.reset_classifier(0, global_pool='')\n else:\n # reset global pool if pool config set, otherwise leave as network default\n reset_kwargs = dict(global_pool=pool) if pool else {}\n self.trunk.reset_classifier(0, **reset_kwargs)\n prev_chs = self.trunk.num_features\n\n head_layers = OrderedDict()\n if pool == 'abs_attn':\n head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim)\n prev_chs = embed_dim\n elif pool == 'rot_attn':\n head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim)\n prev_chs = embed_dim\n else:\n assert proj, 'projection layer needed if non-attention pooling is used.'\n\n # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used\n if proj == 'linear':\n head_layers['drop'] = nn.Dropout(drop)\n head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias)\n elif proj == 'mlp':\n head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias))\n\n self.head = nn.Sequential(head_layers)\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n \"\"\" lock modules\n Args:\n unlocked_groups (int): leave last n layer groups unlocked (default: 0)\n \"\"\"\n if not unlocked_groups:\n # lock full model\n for param in self.trunk.parameters():\n param.requires_grad = False\n if freeze_bn_stats:\n freeze_batch_norm_2d(self.trunk)\n else:\n # NOTE: partial freeze requires latest timm (master) branch and is subject to change\n try:\n # FIXME import here until API stable and in an official release\n from timm.models.helpers import group_parameters, group_modules\n except ImportError:\n raise RuntimeError(\n 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`')\n matcher = self.trunk.group_matcher()\n gparams = group_parameters(self.trunk, matcher)\n max_layer_id = max(gparams.keys())\n max_layer_id = max_layer_id - unlocked_groups\n for group_idx in range(max_layer_id + 1):\n group = gparams[group_idx]\n for param in group:\n self.trunk.get_parameter(param).requires_grad = False\n if freeze_bn_stats:\n gmodules = group_modules(self.trunk, matcher, reverse=True)\n gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}\n freeze_batch_norm_2d(self.trunk, gmodules)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n try:\n self.trunk.set_grad_checkpointing(enable)\n except Exception as e:\n logging.warning('grad checkpointing not supported for this timm image tower, continuing without...')\n\n def forward(self, x):\n x = self.trunk(x)\n x = self.head(x)\n return x\n"
},
{
"path": "src/open_clip/tokenizer.py",
"content": "\"\"\" CLIP tokenizer\n\nCopied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.\n\"\"\"\nimport gzip\nimport html\nimport os\nfrom functools import lru_cache\nfrom typing import Union, List\n\nimport ftfy\nimport regex as re\nimport torch\n\n# https://stackoverflow.com/q/62691279\nimport os\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\n\n@lru_cache()\ndef default_bpe():\n return os.path.join(os.path.dirname(os.path.abspath(__file__)), \"bpe_simple_vocab_16e6.txt.gz\")\n\n\n@lru_cache()\ndef bytes_to_unicode():\n \"\"\"\n Returns list of utf-8 byte and a corresponding list of unicode strings.\n The reversible bpe codes work on unicode strings.\n This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.\n When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.\n This is a significant percentage of your normal, say, 32K bpe vocab.\n To avoid that, we want lookup tables between utf-8 bytes and unicode strings.\n And avoids mapping to whitespace/control characters the bpe code barfs on.\n \"\"\"\n bs = list(range(ord(\"!\"), ord(\"~\")+1))+list(range(ord(\"¡\"), ord(\"¬\")+1))+list(range(ord(\"®\"), ord(\"ÿ\")+1))\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8+n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n Word is represented as tuple of symbols (symbols being variable-length strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r'\\s+', ' ', text)\n text = text.strip()\n return text\n\n\nclass SimpleTokenizer(object):\n def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n merges = gzip.open(bpe_path).read().decode(\"utf-8\").split('\\n')\n merges = merges[1:49152-256-2+1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v+'' for v in vocab]\n for merge in merges:\n vocab.append(''.join(merge))\n if not special_tokens:\n special_tokens = ['', '']\n else:\n special_tokens = ['', ''] + special_tokens\n vocab.extend(special_tokens)\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {t:t for t in special_tokens}\n special = \"|\".join(special_tokens)\n self.pat = re.compile(special + r\"\"\"|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\", re.IGNORECASE)\n\n self.vocab_size = len(self.encoder)\n self.all_special_ids = [self.encoder[t] for t in special_tokens]\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token[:-1]) + ( token[-1] + '',)\n pairs = get_pairs(word)\n\n if not pairs:\n return token+''\n\n while True:\n bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)\n new_word.extend(word[i:j])\n i = j\n except:\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word)-1 and word[i+1] == second:\n new_word.append(first+second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n text = whitespace_clean(basic_clean(text)).lower()\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=\"replace\").replace('', ' ')\n return text\n\n\n_tokenizer = SimpleTokenizer()\n\ndef decode(output_ids: torch.Tensor):\n output_ids = output_ids.cpu().numpy()\n return _tokenizer.decode(output_ids)\n\ndef tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor:\n \"\"\"\n Returns the tokenized representation of given input string(s)\n\n Parameters\n ----------\n texts : Union[str, List[str]]\n An input string or a list of input strings to tokenize\n context_length : int\n The context length to use; all CLIP models use 77 as the context length\n\n Returns\n -------\n A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]\n \"\"\"\n if isinstance(texts, bytes):\n texts = texts.decode()\n\n if isinstance(texts, str):\n texts = [texts]\n\n sot_token = _tokenizer.encoder[\"\"]\n eot_token = _tokenizer.encoder[\"\"]\n all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]\n result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)\n\n for i, tokens in enumerate(all_tokens):\n if len(tokens) > context_length:\n tokens = tokens[:context_length] # Truncate\n tokens[-1] = eot_token\n result[i, :len(tokens)] = torch.tensor(tokens)\n\n return result\n\n\nclass HFTokenizer:\n \"\"\"HuggingFace tokenizer wrapper\"\"\"\n\n def __init__(self, tokenizer_name: str):\n from transformers import AutoTokenizer\n self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)\n\n def save_pretrained(self, dest):\n self.tokenizer.save_pretrained(dest)\n\n def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor:\n # same cleaning as for default tokenizer, except lowercasing\n # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance\n if isinstance(texts, str):\n texts = [texts]\n texts = [whitespace_clean(basic_clean(text)) for text in texts]\n input_ids = self.tokenizer(\n texts,\n return_tensors='pt',\n max_length=context_length,\n padding='max_length',\n truncation=True,\n ).input_ids\n return input_ids\n"
},
{
"path": "src/open_clip/transform.py",
"content": "import warnings\nfrom dataclasses import dataclass, asdict\nfrom typing import Any, Dict, Optional, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torchvision.transforms.functional as F\n\nfrom torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \\\n CenterCrop\n\nfrom .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD\n\n\n@dataclass\nclass AugmentationCfg:\n scale: Tuple[float, float] = (0.9, 1.0)\n ratio: Optional[Tuple[float, float]] = None\n color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None\n interpolation: Optional[str] = None\n re_prob: Optional[float] = None\n re_count: Optional[int] = None\n use_timm: bool = False\n\n\nclass ResizeMaxSize(nn.Module):\n\n def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0):\n super().__init__()\n if not isinstance(max_size, int):\n raise TypeError(f\"Size should be int. Got {type(max_size)}\")\n self.max_size = max_size\n self.interpolation = interpolation\n self.fn = min if fn == 'min' else min\n self.fill = fill\n\n def forward(self, img):\n if isinstance(img, torch.Tensor):\n height, width = img.shape[:2]\n else:\n width, height = img.size\n scale = self.max_size / float(max(height, width))\n if scale != 1.0:\n new_size = tuple(round(dim * scale) for dim in (height, width))\n img = F.resize(img, new_size, self.interpolation)\n pad_h = self.max_size - new_size[0]\n pad_w = self.max_size - new_size[1]\n img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill)\n return img\n\n\ndef _convert_to_rgb(image):\n return image.convert('RGB')\n\n\ndef image_transform(\n image_size: int,\n is_train: bool,\n mean: Optional[Tuple[float, ...]] = None,\n std: Optional[Tuple[float, ...]] = None,\n resize_longest_max: bool = False,\n fill_color: int = 0,\n aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,\n):\n mean = mean or OPENAI_DATASET_MEAN\n if not isinstance(mean, (list, tuple)):\n mean = (mean,) * 3\n\n std = std or OPENAI_DATASET_STD\n if not isinstance(std, (list, tuple)):\n std = (std,) * 3\n\n if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:\n # for square size, pass size as int so that Resize() uses aspect preserving shortest edge\n image_size = image_size[0]\n\n if isinstance(aug_cfg, dict):\n aug_cfg = AugmentationCfg(**aug_cfg)\n else:\n aug_cfg = aug_cfg or AugmentationCfg()\n normalize = Normalize(mean=mean, std=std)\n if is_train:\n aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None}\n use_timm = aug_cfg_dict.pop('use_timm', False)\n if use_timm:\n from timm.data import create_transform # timm can still be optional\n if isinstance(image_size, (tuple, list)):\n assert len(image_size) >= 2\n input_size = (3,) + image_size[-2:]\n else:\n input_size = (3, image_size, image_size)\n # by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time\n aug_cfg_dict.setdefault('interpolation', 'random')\n aug_cfg_dict.setdefault('color_jitter', None) # disable by default\n train_transform = create_transform(\n input_size=input_size,\n is_training=True,\n hflip=0.,\n mean=mean,\n std=std,\n re_mode='pixel',\n **aug_cfg_dict,\n )\n else:\n train_transform = Compose([\n RandomResizedCrop(\n image_size,\n scale=aug_cfg_dict.pop('scale'),\n interpolation=InterpolationMode.BICUBIC,\n ),\n _convert_to_rgb,\n ToTensor(),\n normalize,\n ])\n if aug_cfg_dict:\n warnings.warn(f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).')\n return train_transform\n else:\n if resize_longest_max:\n transforms = [\n ResizeMaxSize(image_size, fill=fill_color)\n ]\n else:\n transforms = [\n Resize(image_size, interpolation=InterpolationMode.BICUBIC),\n CenterCrop(image_size),\n ]\n transforms.extend([\n _convert_to_rgb,\n ToTensor(),\n normalize,\n ])\n return Compose(transforms)\n"
},
{
"path": "src/open_clip/transformer.py",
"content": "from collections import OrderedDict\nimport math\nfrom typing import Callable, Optional, Sequence, Tuple\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.utils.checkpoint import checkpoint\n\nfrom .utils import to_2tuple\n\n\nclass LayerNormFp32(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).\"\"\"\n\n def forward(self, x: torch.Tensor):\n orig_type = x.dtype\n x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps)\n return x.to(orig_type)\n\n\nclass LayerNorm(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm (with cast back to input dtype).\"\"\"\n\n def forward(self, x: torch.Tensor):\n orig_type = x.dtype\n x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\n return x.to(orig_type)\n\n\nclass QuickGELU(nn.Module):\n # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass LayerScale(nn.Module):\n def __init__(self, dim, init_values=1e-5, inplace=False):\n super().__init__()\n self.inplace = inplace\n self.gamma = nn.Parameter(init_values * torch.ones(dim))\n\n def forward(self, x):\n return x.mul_(self.gamma) if self.inplace else x * self.gamma\n\n\nclass PatchDropout(nn.Module):\n \"\"\"\n https://arxiv.org/abs/2212.00794\n \"\"\"\n\n def __init__(self, prob, exclude_first_token=True):\n super().__init__()\n assert 0 <= prob < 1.\n self.prob = prob\n self.exclude_first_token = exclude_first_token # exclude CLS token\n\n def forward(self, x):\n if not self.training or self.prob == 0.:\n return x\n\n if self.exclude_first_token:\n cls_tokens, x = x[:, :1], x[:, 1:]\n else:\n cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])\n\n batch = x.size()[0]\n num_tokens = x.size()[1]\n\n batch_indices = torch.arange(batch)\n batch_indices = batch_indices[..., None]\n\n keep_prob = 1 - self.prob\n num_patches_keep = max(1, int(num_tokens * keep_prob))\n\n rand = torch.randn(batch, num_tokens)\n patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices\n\n x = x[batch_indices, patch_indices_keep]\n\n if self.exclude_first_token:\n x = torch.cat((cls_tokens, x), dim=1)\n\n return x\n\n\nclass Attention(nn.Module):\n def __init__(\n self,\n dim,\n num_heads=8,\n qkv_bias=True,\n scaled_cosine=False,\n scale_heads=False,\n logit_scale_max=math.log(1. / 0.01),\n attn_drop=0.,\n proj_drop=0.\n ):\n super().__init__()\n self.scaled_cosine = scaled_cosine\n self.scale_heads = scale_heads\n assert dim % num_heads == 0, 'dim should be divisible by num_heads'\n self.num_heads = num_heads\n self.head_dim = dim // num_heads\n self.scale = self.head_dim ** -0.5\n self.logit_scale_max = logit_scale_max\n\n # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original\n self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)\n if qkv_bias:\n self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))\n else:\n self.in_proj_bias = None\n\n if self.scaled_cosine:\n self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))\n else:\n self.logit_scale = None\n self.attn_drop = nn.Dropout(attn_drop)\n if self.scale_heads:\n self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))\n else:\n self.head_scale = None\n self.out_proj = nn.Linear(dim, dim)\n self.out_drop = nn.Dropout(proj_drop)\n\n def forward(self, x, attn_mask: Optional[torch.Tensor] = None):\n L, N, C = x.shape\n q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1)\n q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\n k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\n v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\n\n if self.logit_scale is not None:\n attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))\n logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()\n attn = attn.view(N, self.num_heads, L, L) * logit_scale\n attn = attn.view(-1, L, L)\n else:\n q = q * self.scale\n attn = torch.bmm(q, k.transpose(-1, -2))\n\n if attn_mask is not None:\n if attn_mask.dtype == torch.bool:\n new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)\n new_attn_mask.masked_fill_(attn_mask, float(\"-inf\"))\n attn_mask = new_attn_mask\n attn += attn_mask\n\n attn = attn.softmax(dim=-1)\n attn = self.attn_drop(attn)\n\n x = torch.bmm(attn, v)\n if self.head_scale is not None:\n x = x.view(N, self.num_heads, L, C) * self.head_scale\n x = x.view(-1, L, C)\n x = x.transpose(0, 1).reshape(L, N, C)\n x = self.out_proj(x)\n x = self.out_drop(x)\n return x\n\n\nclass AttentionalPooler(nn.Module):\n def __init__(\n self,\n d_model: int,\n context_dim: int,\n n_head: int = 8,\n n_queries: int = 256,\n norm_layer: Callable = LayerNorm\n ):\n super().__init__()\n self.query = nn.Parameter(torch.randn(n_queries, d_model))\n self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim)\n self.ln_q = norm_layer(d_model)\n self.ln_k = norm_layer(context_dim)\n\n def forward(self, x: torch.Tensor):\n x = self.ln_k(x).permute(1, 0, 2) # NLD -> LND\n N = x.shape[1]\n q = self.ln_q(self.query)\n out = self.attn(self._repeat(q, N), x, x, need_weights=False)[0]\n return out.permute(1, 0, 2) # LND -> NLD\n\n def _repeat(self, query, N: int):\n return query.unsqueeze(1).repeat(1, N, 1)\n\n\nclass ResidualAttentionBlock(nn.Module):\n def __init__(\n self,\n d_model: int,\n n_head: int,\n mlp_ratio: float = 4.0,\n ls_init_value: float = None,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = LayerNorm,\n is_cross_attention: bool = False,\n ):\n super().__init__()\n\n self.ln_1 = norm_layer(d_model)\n self.attn = nn.MultiheadAttention(d_model, n_head)\n self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()\n if is_cross_attention:\n self.ln_1_kv = norm_layer(d_model)\n\n self.ln_2 = norm_layer(d_model)\n mlp_width = int(d_model * mlp_ratio)\n self.mlp = nn.Sequential(OrderedDict([\n (\"c_fc\", nn.Linear(d_model, mlp_width)),\n (\"gelu\", act_layer()),\n (\"c_proj\", nn.Linear(mlp_width, d_model))\n ]))\n self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()\n\n def attention(\n self,\n q_x: torch.Tensor,\n k_x: Optional[torch.Tensor] = None,\n v_x: Optional[torch.Tensor] = None,\n attn_mask: Optional[torch.Tensor] = None,\n ):\n k_x = k_x if k_x is not None else q_x\n v_x = v_x if v_x is not None else q_x\n\n attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None\n return self.attn(\n q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask\n )[0]\n\n def forward(\n self,\n q_x: torch.Tensor,\n k_x: Optional[torch.Tensor] = None,\n v_x: Optional[torch.Tensor] = None,\n attn_mask: Optional[torch.Tensor] = None,\n ):\n k_x = self.ln_1_kv(k_x) if hasattr(self, \"ln_1_kv\") and k_x is not None else None\n v_x = self.ln_1_kv(v_x) if hasattr(self, \"ln_1_kv\") and v_x is not None else None\n\n x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask))\n x = x + self.ls_2(self.mlp(self.ln_2(x)))\n return x\n\n\nclass CustomResidualAttentionBlock(nn.Module):\n def __init__(\n self,\n d_model: int,\n n_head: int,\n mlp_ratio: float = 4.0,\n ls_init_value: float = None,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = LayerNorm,\n scale_cosine_attn: bool = False,\n scale_heads: bool = False,\n scale_attn: bool = False,\n scale_fc: bool = False,\n ):\n super().__init__()\n\n self.ln_1 = norm_layer(d_model)\n self.attn = Attention(\n d_model, n_head,\n scaled_cosine=scale_cosine_attn,\n scale_heads=scale_heads,\n )\n self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity()\n self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()\n\n self.ln_2 = norm_layer(d_model)\n mlp_width = int(d_model * mlp_ratio)\n self.mlp = nn.Sequential(OrderedDict([\n (\"c_fc\", nn.Linear(d_model, mlp_width)),\n ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()),\n (\"gelu\", act_layer()),\n (\"c_proj\", nn.Linear(mlp_width, d_model))\n ]))\n self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()\n\n def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):\n x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask)))\n x = x + self.ls_2(self.mlp(self.ln_2(x)))\n return x\n\n\nclass Transformer(nn.Module):\n def __init__(\n self,\n width: int,\n layers: int,\n heads: int,\n mlp_ratio: float = 4.0,\n ls_init_value: float = None,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = LayerNorm,\n ):\n super().__init__()\n self.width = width\n self.layers = layers\n self.grad_checkpointing = False\n\n self.resblocks = nn.ModuleList([\n ResidualAttentionBlock(\n width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer)\n for _ in range(layers)\n ])\n\n def get_cast_dtype(self) -> torch.dtype:\n return self.resblocks[0].mlp.c_fc.weight.dtype\n\n def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):\n for r in self.resblocks:\n if self.grad_checkpointing and not torch.jit.is_scripting():\n # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372\n x = checkpoint(r, x, None, None, attn_mask)\n else:\n x = r(x, attn_mask=attn_mask)\n return x\n\n\nclass VisionTransformer(nn.Module):\n output_tokens: torch.jit.Final[bool]\n\n def __init__(\n self,\n image_size: int,\n patch_size: int,\n width: int,\n layers: int,\n heads: int,\n mlp_ratio: float,\n ls_init_value: float = None,\n global_average_pool: bool = False,\n attentional_pool: bool = False,\n n_queries: int = 256,\n attn_pooler_heads: int = 8,\n output_dim: int = 512,\n patch_dropout: float = 0.,\n input_patchnorm: bool = False,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = LayerNorm,\n output_tokens: bool = False\n ):\n super().__init__()\n self.output_tokens = output_tokens\n image_height, image_width = self.image_size = to_2tuple(image_size)\n patch_height, patch_width = self.patch_size = to_2tuple(patch_size)\n self.grid_size = (image_height // patch_height, image_width // patch_width)\n self.output_dim = output_dim\n\n # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1\n self.input_patchnorm = input_patchnorm\n\n if input_patchnorm:\n patch_input_dim = patch_height * patch_width * 3\n self.patchnorm_pre_ln = LayerNorm(patch_input_dim)\n self.conv1 = nn.Linear(patch_input_dim, width)\n else:\n self.patchnorm_pre_ln = nn.Identity()\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)\n\n # class embeddings and positional embeddings\n scale = width ** -0.5\n self.class_embedding = nn.Parameter(scale * torch.randn(width))\n self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width))\n\n # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn\n self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()\n\n self.ln_pre = norm_layer(width)\n self.transformer = Transformer(\n width,\n layers,\n heads,\n mlp_ratio,\n ls_init_value=ls_init_value,\n act_layer=act_layer,\n norm_layer=norm_layer,\n )\n\n self.global_average_pool = global_average_pool\n if attentional_pool:\n self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries)\n self.ln_post = norm_layer(output_dim)\n self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim))\n else:\n self.attn_pool = None\n self.ln_post = norm_layer(width)\n self.proj = nn.Parameter(scale * torch.randn(width, output_dim))\n\n self.init_parameters()\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n for param in self.parameters():\n param.requires_grad = False\n\n if unlocked_groups != 0:\n groups = [\n [\n self.conv1,\n self.class_embedding,\n self.positional_embedding,\n self.ln_pre,\n ],\n *self.transformer.resblocks[:-1],\n [\n self.transformer.resblocks[-1],\n self.ln_post,\n ],\n self.proj,\n ]\n\n def _unlock(x):\n if isinstance(x, Sequence):\n for g in x:\n _unlock(g)\n else:\n if isinstance(x, torch.nn.Parameter):\n x.requires_grad = True\n else:\n for p in x.parameters():\n p.requires_grad = True\n\n _unlock(groups[-unlocked_groups:])\n\n def init_parameters(self):\n # FIXME OpenAI CLIP did not define an init for the VisualTransformer\n # TODO experiment if default PyTorch init, below, or alternate init is best.\n\n # nn.init.normal_(self.class_embedding, std=self.scale)\n # nn.init.normal_(self.positional_embedding, std=self.scale)\n #\n # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)\n # attn_std = self.transformer.width ** -0.5\n # fc_std = (2 * self.transformer.width) ** -0.5\n # for block in self.transformer.resblocks:\n # nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n # nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n # nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n # nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n #\n # if self.text_projection is not None:\n # nn.init.normal_(self.text_projection, std=self.scale)\n pass\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.transformer.grad_checkpointing = enable\n\n def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n if self.global_average_pool:\n return x.mean(dim=1), x\n else:\n return x[:, 0], x[:, 1:]\n\n def forward(self, x: torch.Tensor):\n\n # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1\n if self.input_patchnorm:\n # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')\n x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1])\n x = x.permute(0, 2, 4, 1, 3, 5)\n x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1)\n x = self.patchnorm_pre_ln(x)\n x = self.conv1(x)\n else:\n x = self.conv1(x) # shape = [*, width, grid, grid]\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\n\n # class embeddings and positional embeddings\n x = torch.cat(\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\n x = x + self.positional_embedding.to(x.dtype)\n\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\n x = self.patch_dropout(x)\n x = self.ln_pre(x)\n\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n\n if self.attn_pool is not None:\n x = self.attn_pool(x)\n x = self.ln_post(x)\n pooled, tokens = self._global_pool(x)\n else:\n pooled, tokens = self._global_pool(x)\n pooled = self.ln_post(pooled)\n\n if self.proj is not None:\n pooled = pooled @ self.proj\n\n if self.output_tokens:\n return pooled, tokens\n \n return pooled\n\n\nclass TextTransformer(nn.Module):\n output_tokens: torch.jit.Final[bool]\n\n def __init__(\n self,\n context_length: int = 77,\n vocab_size: int = 49408,\n width: int = 512,\n heads: int = 8,\n layers: int = 12,\n ls_init_value: float = None,\n output_dim: int = 512,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = LayerNorm,\n embed_cls: bool = False,\n pad_id: int = 0,\n output_tokens: bool = False,\n ):\n super().__init__()\n self.output_tokens = output_tokens\n self.num_pos = self.context_length = context_length\n self.vocab_size = vocab_size\n self.width = width\n self.output_dim = output_dim\n self.heads = heads\n self.pad_id = pad_id\n\n self.text_projection = nn.Parameter(torch.empty(width, output_dim))\n\n if embed_cls:\n self.cls_emb = nn.Parameter(torch.empty(width))\n self.num_pos += 1\n else:\n self.cls_emb = None\n\n self.token_embedding = nn.Embedding(vocab_size, width)\n self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width))\n self.transformer = Transformer(\n width=width,\n layers=layers,\n heads=heads,\n ls_init_value=ls_init_value,\n act_layer=act_layer,\n norm_layer=norm_layer,\n )\n self.ln_final = norm_layer(width)\n\n self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)\n\n self.init_parameters()\n\n def init_parameters(self):\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n nn.init.normal_(self.positional_embedding, std=0.01)\n if self.cls_emb is not None:\n nn.init.normal_(self.cls_emb, std=0.01)\n\n proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)\n attn_std = self.transformer.width ** -0.5\n fc_std = (2 * self.transformer.width) ** -0.5\n for block in self.transformer.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n if self.text_projection is not None:\n nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.transformer.grad_checkpointing = enable\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.num_pos, self.num_pos)\n mask.fill_(float(\"-inf\"))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def build_cls_mask(self, text, cast_dtype: torch.dtype):\n cls_mask = (text != self.pad_id).unsqueeze(1)\n cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0)\n additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device)\n additive_mask.fill_(0)\n additive_mask.masked_fill_(~cls_mask, float(\"-inf\"))\n additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0)\n return additive_mask\n\n def _repeat(self, t, N: int):\n return t.reshape(1, 1, -1).repeat(N, 1, 1)\n\n def forward(self, text):\n cast_dtype = self.transformer.get_cast_dtype()\n seq_len = text.shape[1]\n\n x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model]\n attn_mask = self.attn_mask\n if self.cls_emb is not None:\n seq_len += 1\n x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1)\n cls_mask = self.build_cls_mask(text, cast_dtype)\n attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len]\n\n x = x + self.positional_embedding[:seq_len].to(cast_dtype)\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x, attn_mask=attn_mask)\n x = x.permute(1, 0, 2) # LND -> NLD\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n if self.cls_emb is not None:\n pooled, tokens = x[:, -1], x[:, :-1]\n pooled = self.ln_final(pooled)\n else:\n x = self.ln_final(x)\n pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x\n\n if self.text_projection is not None:\n pooled = pooled @ self.text_projection\n\n if self.output_tokens:\n return pooled, tokens\n\n return pooled\n\n\nclass MultimodalTransformer(Transformer):\n def __init__(\n self,\n width: int,\n layers: int,\n heads: int,\n context_length: int = 77,\n mlp_ratio: float = 4.0,\n ls_init_value: float = None,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = LayerNorm,\n output_dim: int = 512,\n ):\n\n super().__init__(\n width=width,\n layers=layers,\n heads=heads,\n mlp_ratio=mlp_ratio,\n ls_init_value=ls_init_value,\n act_layer=act_layer,\n norm_layer=norm_layer,\n )\n self.context_length = context_length\n self.cross_attn = nn.ModuleList([\n ResidualAttentionBlock(\n width,\n heads,\n mlp_ratio,\n ls_init_value=ls_init_value,\n act_layer=act_layer,\n norm_layer=norm_layer,\n is_cross_attention=True,\n )\n for _ in range(layers)\n ])\n\n self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)\n\n self.ln_final = norm_layer(width)\n self.text_projection = nn.Parameter(torch.empty(width, output_dim))\n\n def init_parameters(self):\n proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)\n attn_std = self.transformer.width ** -0.5\n fc_std = (2 * self.transformer.width) ** -0.5\n for block in self.transformer.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n for block in self.transformer.cross_attn:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n if self.text_projection is not None:\n nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float(\"-inf\"))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(self, image_embs, text_embs):\n text_embs = text_embs.permute(1, 0, 2) # NLD -> LNDsq\n image_embs = image_embs.permute(1, 0, 2) # NLD -> LND\n seq_len = text_embs.shape[0]\n\n for resblock, cross_attn in zip(self.resblocks, self.cross_attn):\n if self.grad_checkpointing and not torch.jit.is_scripting():\n # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372\n text_embs = checkpoint(resblock, text_embs, None, None, self.attn_mask[:seq_len, :seq_len])\n text_embs = checkpoint(cross_attn, text_embs, image_embs, image_embs, None)\n else:\n text_embs = resblock(text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len])\n text_embs = cross_attn(text_embs, k_x=image_embs, v_x=image_embs)\n\n x = text_embs.permute(1, 0, 2) # LND -> NLD\n x = self.ln_final(x)\n\n if self.text_projection is not None:\n x = x @ self.text_projection\n\n return x\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.grad_checkpointing = enable\n"
},
{
"path": "src/open_clip/utils.py",
"content": "from itertools import repeat\nimport collections.abc\n\nfrom torch import nn as nn\nfrom torchvision.ops.misc import FrozenBatchNorm2d\n\n\ndef freeze_batch_norm_2d(module, module_match={}, name=''):\n \"\"\"\n Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is\n itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and\n returned. Otherwise, the module is walked recursively and submodules are converted in place.\n\n Args:\n module (torch.nn.Module): Any PyTorch module.\n module_match (dict): Dictionary of full module names to freeze (all if empty)\n name (str): Full module name (prefix)\n\n Returns:\n torch.nn.Module: Resulting module\n\n Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762\n \"\"\"\n res = module\n is_match = True\n if module_match:\n is_match = name in module_match\n if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)):\n res = FrozenBatchNorm2d(module.num_features)\n res.num_features = module.num_features\n res.affine = module.affine\n if module.affine:\n res.weight.data = module.weight.data.clone().detach()\n res.bias.data = module.bias.data.clone().detach()\n res.running_mean.data = module.running_mean.data\n res.running_var.data = module.running_var.data\n res.eps = module.eps\n else:\n for child_name, child in module.named_children():\n full_child_name = '.'.join([name, child_name]) if name else child_name\n new_child = freeze_batch_norm_2d(child, module_match, full_child_name)\n if new_child is not child:\n res.add_module(child_name, new_child)\n return res\n\n\n# From PyTorch internals\ndef _ntuple(n):\n def parse(x):\n if isinstance(x, collections.abc.Iterable):\n return x\n return tuple(repeat(x, n))\n return parse\n\n\nto_1tuple = _ntuple(1)\nto_2tuple = _ntuple(2)\nto_3tuple = _ntuple(3)\nto_4tuple = _ntuple(4)\nto_ntuple = lambda n, x: _ntuple(n)(x)\n"
},
{
"path": "src/open_clip/version.py",
"content": "__version__ = '2.16.0'\n"
},
{
"path": "src/training/.gitignore",
"content": "logs/\n"
},
{
"path": "src/training/__init__.py",
"content": ""
},
{
"path": "src/training/data.py",
"content": "import ast\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport sys\nimport time\nimport braceexpand\nfrom dataclasses import dataclass\nfrom multiprocessing import Value\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torchvision.datasets as datasets\nimport webdataset as wds\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader, SubsetRandomSampler, IterableDataset, get_worker_info\nfrom torch.utils.data.distributed import DistributedSampler\nfrom webdataset.filters import _shuffle\nfrom webdataset.tariterators import base_plus_ext, url_opener, tar_file_expander, valid_sample\n\ntry:\n import horovod.torch as hvd\nexcept ImportError:\n hvd = None\n\n\nclass CsvDataset(Dataset):\n def __init__(self, input_filename, transforms, img_key, caption_key, sep=\"\\t\", tokenizer=None):\n logging.debug(f'Loading csv data from {input_filename}.')\n df = pd.read_csv(input_filename, sep=sep)\n\n self.images = df[img_key].tolist()\n self.captions = df[caption_key].tolist()\n self.transforms = transforms\n logging.debug('Done loading data.')\n\n self.tokenize = tokenizer\n\n def __len__(self):\n return len(self.captions)\n\n def __getitem__(self, idx):\n images = self.transforms(Image.open(str(self.images[idx])))\n texts = self.tokenize([str(self.captions[idx])])[0]\n return images, texts\n\n\nclass SharedEpoch:\n def __init__(self, epoch: int = 0):\n self.shared_epoch = Value('i', epoch)\n\n def set_value(self, epoch):\n self.shared_epoch.value = epoch\n\n def get_value(self):\n return self.shared_epoch.value\n\n\n@dataclass\nclass DataInfo:\n dataloader: DataLoader\n sampler: DistributedSampler = None\n shared_epoch: SharedEpoch = None\n\n def set_epoch(self, epoch):\n if self.shared_epoch is not None:\n self.shared_epoch.set_value(epoch)\n if self.sampler is not None and isinstance(self.sampler, DistributedSampler):\n self.sampler.set_epoch(epoch)\n\n\ndef expand_urls(urls, weights=None):\n if weights is None:\n expanded_urls = wds.shardlists.expand_urls(urls)\n return expanded_urls, None\n if isinstance(urls, str):\n urllist = urls.split(\"::\")\n weights = weights.split('::')\n assert len(weights) == len(urllist), f\"Expected the number of data components ({len(urllist)}) and weights({len(weights)}) to match.\"\n weights = [float(weight) for weight in weights]\n all_urls, all_weights = [], []\n for url, weight in zip(urllist, weights):\n expanded_url = list(braceexpand.braceexpand(url))\n expanded_weights = [weight for _ in expanded_url]\n all_urls.extend(expanded_url)\n all_weights.extend(expanded_weights)\n return all_urls, all_weights\n else:\n all_urls = list(urls)\n return all_urls, weights\n\n\ndef get_dataset_size(shards):\n shards_list, _ = expand_urls(shards)\n dir_path = os.path.dirname(shards_list[0])\n sizes_filename = os.path.join(dir_path, 'sizes.json')\n len_filename = os.path.join(dir_path, '__len__')\n if os.path.exists(sizes_filename):\n sizes = json.load(open(sizes_filename, 'r'))\n # Lisa wants the shard sizes under the per_shard key.\n # But this is not always the case.\n if \"per_shard\" in sizes:\n sizes = sizes[\"per_shard\"]\n total_size = sum([int(sizes[os.path.basename(shard)]) for shard in shards_list])\n elif os.path.exists(len_filename):\n # FIXME this used to be eval(open(...)) but that seemed rather unsafe\n total_size = ast.literal_eval(open(len_filename, 'r').read())\n else:\n total_size = None # num samples undefined\n # some common dataset sizes (at time of authors last download)\n # CC3M (train): 2905954\n # CC12M: 10968539\n # LAION-400M: 407332084\n # LAION-2B (english): 2170337258\n num_shards = len(shards_list)\n return total_size, num_shards\n\n\ndef get_imagenet(args, preprocess_fns, split):\n assert split in [\"train\", \"val\", \"v2\"]\n is_train = split == \"train\"\n preprocess_train, preprocess_val = preprocess_fns\n\n if split == \"v2\":\n from imagenetv2_pytorch import ImageNetV2Dataset\n dataset = ImageNetV2Dataset(location=args.imagenet_v2, transform=preprocess_val)\n else:\n if is_train:\n data_path = args.imagenet_train\n preprocess_fn = preprocess_train\n else:\n data_path = args.imagenet_val\n preprocess_fn = preprocess_val\n assert data_path\n\n dataset = datasets.ImageFolder(data_path, transform=preprocess_fn)\n\n if is_train:\n idxs = np.zeros(len(dataset.targets))\n target_array = np.array(dataset.targets)\n k = 50\n for c in range(1000):\n m = target_array == c\n n = len(idxs[m])\n arr = np.zeros(n)\n arr[:k] = 1\n np.random.shuffle(arr)\n idxs[m] = arr\n\n idxs = idxs.astype('int')\n sampler = SubsetRandomSampler(np.where(idxs)[0])\n else:\n sampler = None\n\n dataloader = torch.utils.data.DataLoader(\n dataset,\n batch_size=args.batch_size,\n num_workers=args.workers,\n sampler=sampler,\n )\n\n return DataInfo(dataloader=dataloader, sampler=sampler)\n\n\ndef count_samples(dataloader):\n os.environ[\"WDS_EPOCH\"] = \"0\"\n n_elements, n_batches = 0, 0\n for images, texts in dataloader:\n n_batches += 1\n n_elements += len(images)\n assert len(images) == len(texts)\n return n_elements, n_batches\n\n\ndef filter_no_caption_or_no_image(sample):\n has_caption = any('txt' in key for key in sample)\n has_image = ('png' in sample or 'jpg' in sample or 'jpeg' in sample or 'webp' in sample)\n return has_caption and has_image\n\n\ndef log_and_continue(exn):\n \"\"\"Call in an exception handler to ignore any exception, issue a warning, and continue.\"\"\"\n logging.warning(f'Handling webdataset error ({repr(exn)}). Ignoring.')\n return True\n\n\ndef group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):\n \"\"\"Return function over iterator that groups key, value pairs into samples.\n\n :param keys: function that splits the key into key and extension (base_plus_ext)\n :param lcase: convert suffixes to lower case (Default value = True)\n \"\"\"\n current_sample = None\n for filesample in data:\n assert isinstance(filesample, dict)\n fname, value = filesample[\"fname\"], filesample[\"data\"]\n prefix, suffix = keys(fname)\n if prefix is None:\n continue\n if lcase:\n suffix = suffix.lower()\n # FIXME webdataset version throws if suffix in current_sample, but we have a potential for\n # this happening in the current LAION400m dataset if a tar ends with same prefix as the next\n # begins, rare, but can happen since prefix aren't unique across tar files in that dataset\n if current_sample is None or prefix != current_sample[\"__key__\"] or suffix in current_sample:\n if valid_sample(current_sample):\n yield current_sample\n current_sample = dict(__key__=prefix, __url__=filesample[\"__url__\"])\n if suffixes is None or suffix in suffixes:\n current_sample[suffix] = value\n if valid_sample(current_sample):\n yield current_sample\n\n\ndef tarfile_to_samples_nothrow(src, handler=log_and_continue):\n # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw\n streams = url_opener(src, handler=handler)\n files = tar_file_expander(streams, handler=handler)\n samples = group_by_keys_nothrow(files, handler=handler)\n return samples\n\n\ndef pytorch_worker_seed(increment=0):\n \"\"\"get dataloader worker seed from pytorch\"\"\"\n worker_info = get_worker_info()\n if worker_info is not None:\n # favour using the seed already created for pytorch dataloader workers if it exists\n seed = worker_info.seed\n if increment:\n # space out seed increments so they can't overlap across workers in different iterations\n seed += increment * max(1, worker_info.num_workers)\n return seed\n # fallback to wds rank based seed\n return wds.utils.pytorch_worker_seed()\n\n\n_SHARD_SHUFFLE_SIZE = 2000\n_SHARD_SHUFFLE_INITIAL = 500\n_SAMPLE_SHUFFLE_SIZE = 5000\n_SAMPLE_SHUFFLE_INITIAL = 1000\n\n\nclass detshuffle2(wds.PipelineStage):\n def __init__(\n self,\n bufsize=1000,\n initial=100,\n seed=0,\n epoch=-1,\n ):\n self.bufsize = bufsize\n self.initial = initial\n self.seed = seed\n self.epoch = epoch\n\n def run(self, src):\n if isinstance(self.epoch, SharedEpoch):\n epoch = self.epoch.get_value()\n else:\n # NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)\n # situation as different workers may wrap at different times (or not at all).\n self.epoch += 1\n epoch = self.epoch\n rng = random.Random()\n if self.seed < 0:\n # If seed is negative, we use the worker's seed, this will be different across all nodes/workers\n seed = pytorch_worker_seed(epoch)\n else:\n # This seed to be deterministic AND the same across all nodes/workers in each epoch\n seed = self.seed + epoch\n rng.seed(seed)\n return _shuffle(src, self.bufsize, self.initial, rng)\n\n\nclass ResampledShards2(IterableDataset):\n \"\"\"An iterable dataset yielding a list of urls.\"\"\"\n\n def __init__(\n self,\n urls,\n weights=None,\n nshards=sys.maxsize,\n worker_seed=None,\n deterministic=False,\n epoch=-1,\n ):\n \"\"\"Sample shards from the shard list with replacement.\n\n :param urls: a list of URLs as a Python list or brace notation string\n \"\"\"\n super().__init__()\n urls, weights = expand_urls(urls, weights)\n self.urls = urls\n self.weights = weights\n if self.weights is not None:\n assert len(self.urls) == len(self.weights), f\"Number of urls {len(self.urls)} and weights {len(self.weights)} should match.\"\n assert isinstance(self.urls[0], str)\n self.nshards = nshards\n self.rng = random.Random()\n self.worker_seed = worker_seed\n self.deterministic = deterministic\n self.epoch = epoch\n\n def __iter__(self):\n \"\"\"Return an iterator over the shards.\"\"\"\n if isinstance(self.epoch, SharedEpoch):\n epoch = self.epoch.get_value()\n else:\n # NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train)\n # situation as different workers may wrap at different times (or not at all).\n self.epoch += 1\n epoch = self.epoch\n if self.deterministic:\n # reset seed w/ epoch if deterministic\n if self.worker_seed is None:\n # pytorch worker seed should be deterministic due to being init by arg.seed + rank + worker id\n seed = pytorch_worker_seed(epoch)\n else:\n seed = self.worker_seed() + epoch\n self.rng.seed(seed)\n for _ in range(self.nshards):\n if self.weights is None:\n yield dict(url=self.rng.choice(self.urls))\n else:\n yield dict(url=self.rng.choices(self.urls, weights=self.weights, k=1)[0])\n\n\ndef get_wds_dataset(args, preprocess_img, is_train, epoch=0, floor=False, tokenizer=None):\n input_shards = args.train_data if is_train else args.val_data\n assert input_shards is not None\n resampled = getattr(args, 'dataset_resampled', False) and is_train\n\n num_samples, num_shards = get_dataset_size(input_shards)\n if not num_samples:\n if is_train:\n num_samples = args.train_num_samples\n if not num_samples:\n raise RuntimeError(\n 'Currently, number of dataset samples must be specified for training dataset. '\n 'Please specify via `--train-num-samples` if no dataset length info present.')\n else:\n num_samples = args.val_num_samples or 0 # eval will just exhaust the iterator if not specified\n\n logging.info(\n f\"Finish counting shard total size: {num_samples}.\")\n \n shared_epoch = SharedEpoch(epoch=epoch) # create a shared epoch store to sync epoch to dataloader worker proc\n \n if resampled:\n pipeline = [ResampledShards2(input_shards, weights=args.train_data_upsampling_factors, deterministic=True, epoch=shared_epoch)]\n else:\n assert args.train_data_upsampling_factors is None, \"--train_data_upsampling_factors is only supported when sampling with replacement (together with --dataset-resampled).\"\n pipeline = [wds.SimpleShardList(input_shards)]\n\n # at this point we have an iterator over all the shards\n if is_train:\n if not resampled:\n pipeline.extend([\n detshuffle2(\n bufsize=_SHARD_SHUFFLE_SIZE,\n initial=_SHARD_SHUFFLE_INITIAL,\n seed=args.seed,\n epoch=shared_epoch,\n ),\n wds.split_by_node,\n wds.split_by_worker,\n ])\n pipeline.extend([\n # at this point, we have an iterator over the shards assigned to each worker at each node\n tarfile_to_samples_nothrow, # wds.tarfile_to_samples(handler=log_and_continue),\n wds.shuffle(\n bufsize=_SAMPLE_SHUFFLE_SIZE,\n initial=_SAMPLE_SHUFFLE_INITIAL,\n ),\n ])\n else:\n pipeline.extend([\n wds.split_by_worker,\n # at this point, we have an iterator over the shards assigned to each worker\n wds.tarfile_to_samples(handler=log_and_continue),\n ])\n if args.text_type == 'random':\n\n pipeline.extend([\n wds.select(filter_no_caption_or_no_image),\n wds.decode(\"pilrgb\", handler=log_and_continue),\n wds.rename(image=\"jpg;png;jpeg;webp\",sci=\"sci.txt\", com=\"com.txt\",taxon=\"taxon.txt\", sci_com=\"sci_com.txt\", taxon_com = \"taxon_com.txt\"),\n wds.map_dict(image=preprocess_img, sci=lambda sci: tokenizer(sci)[0], com=lambda com: tokenizer(com)[0], taxon=lambda taxon: tokenizer(taxon)[0], sci_com=lambda sci_com: tokenizer(sci_com)[0], taxon_com=lambda taxon_com: tokenizer(taxon_com)[0]),\n wds.to_tuple(\"image\", \"sci\", \"com\", \"taxon\", \"sci_com\", \"taxon_com\"),\n wds.batched(args.batch_size, partial=not is_train)\n ])\n else:\n pipeline.extend([\n wds.select(filter_no_caption_or_no_image),\n wds.decode(\"pilrgb\", handler=log_and_continue),\n wds.rename(image=\"jpg;png;jpeg;webp\", text=args.text_type+'.txt'),\n wds.map_dict(image=preprocess_img, text=lambda text: tokenizer(text)[0]),\n wds.to_tuple(\"image\", \"text\"),\n wds.batched(args.batch_size, partial=not is_train)\n ])\n\n\n dataset = wds.DataPipeline(*pipeline)\n\n if is_train:\n if not resampled:\n assert num_shards >= args.workers * args.world_size, 'number of shards must be >= total workers'\n # roll over and repeat a few samples to get same number of full batches on each node\n round_fn = math.floor if floor else math.ceil\n global_batch_size = args.batch_size * args.world_size\n num_batches = round_fn(num_samples / global_batch_size)\n num_workers = max(1, args.workers)\n num_worker_batches = round_fn(num_batches / num_workers) # per dataloader worker\n num_batches = num_worker_batches * num_workers\n num_samples = num_batches * global_batch_size\n dataset = dataset.with_epoch(num_worker_batches) # each worker is iterating over this\n else:\n # last batches are partial, eval is done on single (master) node\n num_batches = math.ceil(num_samples / args.batch_size)\n\n dataloader = wds.WebLoader(\n dataset,\n batch_size=None,\n shuffle=False,\n num_workers=args.workers,\n persistent_workers=True,\n )\n\n # FIXME not clear which approach is better, with_epoch before vs after dataloader?\n # hoping to resolve via https://github.com/webdataset/webdataset/issues/169\n # if is_train:\n # # roll over and repeat a few samples to get same number of full batches on each node\n # global_batch_size = args.batch_size * args.world_size\n # num_batches = math.ceil(num_samples / global_batch_size)\n # num_workers = max(1, args.workers)\n # num_batches = math.ceil(num_batches / num_workers) * num_workers\n # num_samples = num_batches * global_batch_size\n # dataloader = dataloader.with_epoch(num_batches)\n # else:\n # # last batches are partial, eval is done on single (master) node\n # num_batches = math.ceil(num_samples / args.batch_size)\n\n # add meta-data to dataloader instance for convenience\n dataloader.num_batches = num_batches\n dataloader.num_samples = num_samples\n\n return DataInfo(dataloader=dataloader, shared_epoch=shared_epoch)\n\n\ndef get_csv_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):\n input_filename = args.train_data if is_train else args.val_data\n assert input_filename\n dataset = CsvDataset(\n input_filename,\n preprocess_fn,\n img_key=args.csv_img_key,\n caption_key=args.csv_caption_key,\n sep=args.csv_separator,\n tokenizer=tokenizer\n )\n num_samples = len(dataset)\n sampler = DistributedSampler(dataset) if args.distributed and is_train else None\n shuffle = is_train and sampler is None\n\n dataloader = DataLoader(\n dataset,\n batch_size=args.batch_size,\n shuffle=shuffle,\n num_workers=args.workers,\n pin_memory=True,\n sampler=sampler,\n drop_last=is_train,\n )\n dataloader.num_samples = num_samples\n dataloader.num_batches = len(dataloader)\n\n return DataInfo(dataloader, sampler)\n\n\nclass SyntheticDataset(Dataset):\n\n def __init__(self, transform=None, image_size=(224, 224), caption=\"Dummy caption\", dataset_size=100, tokenizer=None):\n self.transform = transform\n self.image_size = image_size\n self.caption = caption\n self.image = Image.new('RGB', image_size)\n self.dataset_size = dataset_size\n\n self.preprocess_txt = lambda text: tokenizer(text)[0]\n\n def __len__(self):\n return self.dataset_size\n\n def __getitem__(self, idx):\n if self.transform is not None:\n image = self.transform(self.image)\n return image, self.preprocess_txt(self.caption)\n\n\ndef get_synthetic_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=None):\n image_size = preprocess_fn.transforms[0].size\n dataset = SyntheticDataset(\n transform=preprocess_fn, image_size=image_size, dataset_size=args.train_num_samples, tokenizer=tokenizer)\n num_samples = len(dataset)\n sampler = DistributedSampler(dataset) if args.distributed and is_train else None\n shuffle = is_train and sampler is None\n\n dataloader = DataLoader(\n dataset,\n batch_size=args.batch_size,\n shuffle=shuffle,\n num_workers=args.workers,\n pin_memory=True,\n sampler=sampler,\n drop_last=is_train,\n )\n dataloader.num_samples = num_samples\n dataloader.num_batches = len(dataloader)\n\n return DataInfo(dataloader, sampler)\n\n\ndef get_dataset_fn(data_path, dataset_type):\n if dataset_type == \"webdataset\":\n return get_wds_dataset\n elif dataset_type == \"csv\":\n return get_csv_dataset\n elif dataset_type == \"synthetic\":\n return get_synthetic_dataset\n elif dataset_type == \"auto\":\n ext = data_path.split('.')[-1]\n if ext in ['csv', 'tsv']:\n return get_csv_dataset\n elif ext in ['tar']:\n return get_wds_dataset\n else:\n raise ValueError(\n f\"Tried to figure out dataset type, but failed for extension {ext}.\")\n else:\n raise ValueError(f\"Unsupported dataset type: {dataset_type}\")\n \n\ndef get_data(args, preprocess_fns, epoch=0, tokenizer=None):\n preprocess_train, preprocess_val = preprocess_fns\n data = {}\n\n if args.train_data or args.dataset_type == \"synthetic\":\n data[\"train\"] = get_dataset_fn(args.train_data, args.dataset_type)(\n args, preprocess_train, is_train=True, epoch=epoch, tokenizer=tokenizer)\n\n if args.val_data:\n data[\"val\"] = get_dataset_fn(args.val_data, args.dataset_type)(\n args, preprocess_val, is_train=False, tokenizer=tokenizer)\n\n if args.imagenet_val is not None:\n data[\"imagenet-val\"] = get_imagenet(args, preprocess_fns, \"val\")\n\n if args.imagenet_v2 is not None:\n data[\"imagenet-v2\"] = get_imagenet(args, preprocess_fns, \"v2\")\n\n return data\n"
},
{
"path": "src/training/distributed.py",
"content": "import os\n\nimport torch\nimport torch.distributed as dist\n\ntry:\n import horovod.torch as hvd\nexcept ImportError:\n hvd = None\n\n\ndef is_global_master(args):\n return args.rank == 0\n\n\ndef is_local_master(args):\n return args.local_rank == 0\n\n\ndef is_master(args, local=False):\n return is_local_master(args) if local else is_global_master(args)\n\n\ndef is_using_horovod():\n # NOTE w/ horovod run, OMPI vars should be set, but w/ SLURM PMI vars will be set\n # Differentiating between horovod and DDP use via SLURM may not be possible, so horovod arg still required...\n ompi_vars = [\"OMPI_COMM_WORLD_RANK\", \"OMPI_COMM_WORLD_SIZE\"]\n pmi_vars = [\"PMI_RANK\", \"PMI_SIZE\"]\n if all([var in os.environ for var in ompi_vars]) or all([var in os.environ for var in pmi_vars]):\n return True\n else:\n return False\n\n\ndef is_using_distributed():\n if 'WORLD_SIZE' in os.environ:\n return int(os.environ['WORLD_SIZE']) > 1\n if 'SLURM_NTASKS' in os.environ:\n return int(os.environ['SLURM_NTASKS']) > 1\n return False\n\n\ndef world_info_from_env():\n local_rank = 0\n for v in ('LOCAL_RANK', 'MPI_LOCALRANKID', 'SLURM_LOCALID', 'OMPI_COMM_WORLD_LOCAL_RANK'):\n if v in os.environ:\n local_rank = int(os.environ[v])\n break\n global_rank = 0\n for v in ('RANK', 'PMI_RANK', 'SLURM_PROCID', 'OMPI_COMM_WORLD_RANK'):\n if v in os.environ:\n global_rank = int(os.environ[v])\n break\n world_size = 1\n for v in ('WORLD_SIZE', 'PMI_SIZE', 'SLURM_NTASKS', 'OMPI_COMM_WORLD_SIZE'):\n if v in os.environ:\n world_size = int(os.environ[v])\n break\n\n return local_rank, global_rank, world_size\n\n\ndef init_distributed_device(args):\n # Distributed training = training on more than one GPU.\n # Works in both single and multi-node scenarios.\n args.distributed = False\n args.world_size = 1\n args.rank = 0 # global rank\n args.local_rank = 0\n if args.horovod:\n assert hvd is not None, \"Horovod is not installed\"\n hvd.init()\n args.local_rank = int(hvd.local_rank())\n args.rank = hvd.rank()\n args.world_size = hvd.size()\n args.distributed = True\n os.environ['LOCAL_RANK'] = str(args.local_rank)\n os.environ['RANK'] = str(args.rank)\n os.environ['WORLD_SIZE'] = str(args.world_size)\n elif is_using_distributed():\n if 'SLURM_PROCID' in os.environ:\n # DDP via SLURM\n args.local_rank, args.rank, args.world_size = world_info_from_env()\n # SLURM var -> torch.distributed vars in case needed\n os.environ['LOCAL_RANK'] = str(args.local_rank)\n os.environ['RANK'] = str(args.rank)\n os.environ['WORLD_SIZE'] = str(args.world_size)\n torch.distributed.init_process_group(\n backend=args.dist_backend,\n init_method=args.dist_url,\n world_size=args.world_size,\n rank=args.rank,\n )\n else:\n # DDP via torchrun, torch.distributed.launch\n args.local_rank, _, _ = world_info_from_env()\n torch.distributed.init_process_group(\n backend=args.dist_backend,\n init_method=args.dist_url)\n args.world_size = torch.distributed.get_world_size()\n args.rank = torch.distributed.get_rank()\n args.distributed = True\n\n if torch.cuda.is_available():\n if args.distributed and not args.no_set_device_rank:\n device = 'cuda:%d' % args.local_rank\n else:\n device = 'cuda:0'\n torch.cuda.set_device(device)\n else:\n device = 'cpu'\n args.device = device\n device = torch.device(device)\n return device\n\n\ndef broadcast_object(args, obj, src=0):\n # broadcast a pickle-able python object from rank-0 to all ranks\n if args.horovod:\n return hvd.broadcast_object(obj, root_rank=src)\n else:\n if args.rank == src:\n objects = [obj]\n else:\n objects = [None]\n dist.broadcast_object_list(objects, src=src)\n return objects[0]\n\n\ndef all_gather_object(args, obj, dst=0):\n # gather a pickle-able python object across all ranks\n if args.horovod:\n return hvd.allgather_object(obj)\n else:\n objects = [None for _ in range(args.world_size)]\n dist.all_gather_object(objects, obj)\n return objects\n"
},
{
"path": "src/training/file_utils.py",
"content": "import logging\nimport os\nimport multiprocessing\nimport subprocess\nimport time\nimport fsspec\nimport torch\nfrom tqdm import tqdm\n\ndef remote_sync_s3(local_dir, remote_dir):\n # skip epoch_latest which can change during sync.\n result = subprocess.run([\"aws\", \"s3\", \"sync\", local_dir, remote_dir, '--exclude', '*epoch_latest.pt'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n if result.returncode != 0:\n logging.error(f\"Error: Failed to sync with S3 bucket {result.stderr.decode('utf-8')}\")\n return False\n \n logging.info(f\"Successfully synced with S3 bucket\")\n return True\n\ndef remote_sync_fsspec(local_dir, remote_dir):\n # FIXME currently this is slow and not recommended. Look into speeding up.\n a = fsspec.get_mapper(local_dir)\n b = fsspec.get_mapper(remote_dir)\n\n for k in a:\n # skip epoch_latest which can change during sync.\n if 'epoch_latest.pt' in k:\n continue\n\n logging.info(f'Attempting to sync {k}')\n if k in b and len(a[k]) == len(b[k]):\n logging.debug(f'Skipping remote sync for {k}.')\n continue\n\n try:\n logging.info(f'Successful sync for {k}.')\n b[k] = a[k]\n except Exception as e:\n logging.info(f'Error during remote sync for {k}: {e}')\n return False\n\n return True\n\ndef remote_sync(local_dir, remote_dir, protocol):\n logging.info('Starting remote sync.')\n if protocol == 's3':\n return remote_sync_s3(local_dir, remote_dir)\n elif protocol == 'fsspec':\n return remote_sync_fsspec(local_dir, remote_dir)\n else:\n logging.error('Remote protocol not known')\n return False\n\ndef keep_running_remote_sync(sync_every, local_dir, remote_dir, protocol):\n while True:\n time.sleep(sync_every)\n remote_sync(local_dir, remote_dir, protocol)\n\ndef start_sync_process(sync_every, local_dir, remote_dir, protocol):\n p = multiprocessing.Process(target=keep_running_remote_sync, args=(sync_every, local_dir, remote_dir, protocol))\n return p\n\n# Note: we are not currently using this save function.\ndef pt_save(pt_obj, file_path):\n of = fsspec.open(file_path, \"wb\")\n with of as f:\n torch.save(pt_obj, file_path)\n\ndef pt_load(file_path, map_location=None):\n if file_path.startswith('s3'):\n logging.info('Loading remote checkpoint, which may take a bit.')\n of = fsspec.open(file_path, \"rb\")\n with of as f:\n out = torch.load(f, map_location=map_location)\n return out\n\ndef check_exists(file_path):\n try:\n with fsspec.open(file_path):\n pass\n except FileNotFoundError:\n return False\n return True\n"
},
{
"path": "src/training/imagenet_zeroshot_data.py",
"content": "\n\nimagenet_classnames = [\"tench\", \"goldfish\", \"great white shark\", \"tiger shark\", \"hammerhead shark\", \"electric ray\",\n \"stingray\", \"rooster\", \"hen\", \"ostrich\", \"brambling\", \"goldfinch\", \"house finch\", \"junco\",\n \"indigo bunting\", \"American robin\", \"bulbul\", \"jay\", \"magpie\", \"chickadee\", \"American dipper\",\n \"kite (bird of prey)\", \"bald eagle\", \"vulture\", \"great grey owl\", \"fire salamander\",\n \"smooth newt\", \"newt\", \"spotted salamander\", \"axolotl\", \"American bullfrog\", \"tree frog\",\n \"tailed frog\", \"loggerhead sea turtle\", \"leatherback sea turtle\", \"mud turtle\", \"terrapin\",\n \"box turtle\", \"banded gecko\", \"green iguana\", \"Carolina anole\",\n \"desert grassland whiptail lizard\", \"agama\", \"frilled-necked lizard\", \"alligator lizard\",\n \"Gila monster\", \"European green lizard\", \"chameleon\", \"Komodo dragon\", \"Nile crocodile\",\n \"American alligator\", \"triceratops\", \"worm snake\", \"ring-necked snake\",\n \"eastern hog-nosed snake\", \"smooth green snake\", \"kingsnake\", \"garter snake\", \"water snake\",\n \"vine snake\", \"night snake\", \"boa constrictor\", \"African rock python\", \"Indian cobra\",\n \"green mamba\", \"sea snake\", \"Saharan horned viper\", \"eastern diamondback rattlesnake\",\n \"sidewinder rattlesnake\", \"trilobite\", \"harvestman\", \"scorpion\", \"yellow garden spider\",\n \"barn spider\", \"European garden spider\", \"southern black widow\", \"tarantula\", \"wolf spider\",\n \"tick\", \"centipede\", \"black grouse\", \"ptarmigan\", \"ruffed grouse\", \"prairie grouse\", \"peafowl\",\n \"quail\", \"partridge\", \"african grey parrot\", \"macaw\", \"sulphur-crested cockatoo\", \"lorikeet\",\n \"coucal\", \"bee eater\", \"hornbill\", \"hummingbird\", \"jacamar\", \"toucan\", \"duck\",\n \"red-breasted merganser\", \"goose\", \"black swan\", \"tusker\", \"echidna\", \"platypus\", \"wallaby\",\n \"koala\", \"wombat\", \"jellyfish\", \"sea anemone\", \"brain coral\", \"flatworm\", \"nematode\", \"conch\",\n \"snail\", \"slug\", \"sea slug\", \"chiton\", \"chambered nautilus\", \"Dungeness crab\", \"rock crab\",\n \"fiddler crab\", \"red king crab\", \"American lobster\", \"spiny lobster\", \"crayfish\", \"hermit crab\",\n \"isopod\", \"white stork\", \"black stork\", \"spoonbill\", \"flamingo\", \"little blue heron\",\n \"great egret\", \"bittern bird\", \"crane bird\", \"limpkin\", \"common gallinule\", \"American coot\",\n \"bustard\", \"ruddy turnstone\", \"dunlin\", \"common redshank\", \"dowitcher\", \"oystercatcher\",\n \"pelican\", \"king penguin\", \"albatross\", \"grey whale\", \"killer whale\", \"dugong\", \"sea lion\",\n \"Chihuahua\", \"Japanese Chin\", \"Maltese\", \"Pekingese\", \"Shih Tzu\", \"King Charles Spaniel\",\n \"Papillon\", \"toy terrier\", \"Rhodesian Ridgeback\", \"Afghan Hound\", \"Basset Hound\", \"Beagle\",\n \"Bloodhound\", \"Bluetick Coonhound\", \"Black and Tan Coonhound\", \"Treeing Walker Coonhound\",\n \"English foxhound\", \"Redbone Coonhound\", \"borzoi\", \"Irish Wolfhound\", \"Italian Greyhound\",\n \"Whippet\", \"Ibizan Hound\", \"Norwegian Elkhound\", \"Otterhound\", \"Saluki\", \"Scottish Deerhound\",\n \"Weimaraner\", \"Staffordshire Bull Terrier\", \"American Staffordshire Terrier\",\n \"Bedlington Terrier\", \"Border Terrier\", \"Kerry Blue Terrier\", \"Irish Terrier\",\n \"Norfolk Terrier\", \"Norwich Terrier\", \"Yorkshire Terrier\", \"Wire Fox Terrier\",\n \"Lakeland Terrier\", \"Sealyham Terrier\", \"Airedale Terrier\", \"Cairn Terrier\",\n \"Australian Terrier\", \"Dandie Dinmont Terrier\", \"Boston Terrier\", \"Miniature Schnauzer\",\n \"Giant Schnauzer\", \"Standard Schnauzer\", \"Scottish Terrier\", \"Tibetan Terrier\",\n \"Australian Silky Terrier\", \"Soft-coated Wheaten Terrier\", \"West Highland White Terrier\",\n \"Lhasa Apso\", \"Flat-Coated Retriever\", \"Curly-coated Retriever\", \"Golden Retriever\",\n \"Labrador Retriever\", \"Chesapeake Bay Retriever\", \"German Shorthaired Pointer\", \"Vizsla\",\n \"English Setter\", \"Irish Setter\", \"Gordon Setter\", \"Brittany dog\", \"Clumber Spaniel\",\n \"English Springer Spaniel\", \"Welsh Springer Spaniel\", \"Cocker Spaniel\", \"Sussex Spaniel\",\n \"Irish Water Spaniel\", \"Kuvasz\", \"Schipperke\", \"Groenendael dog\", \"Malinois\", \"Briard\",\n \"Australian Kelpie\", \"Komondor\", \"Old English Sheepdog\", \"Shetland Sheepdog\", \"collie\",\n \"Border Collie\", \"Bouvier des Flandres dog\", \"Rottweiler\", \"German Shepherd Dog\", \"Dobermann\",\n \"Miniature Pinscher\", \"Greater Swiss Mountain Dog\", \"Bernese Mountain Dog\",\n \"Appenzeller Sennenhund\", \"Entlebucher Sennenhund\", \"Boxer\", \"Bullmastiff\", \"Tibetan Mastiff\",\n \"French Bulldog\", \"Great Dane\", \"St. Bernard\", \"husky\", \"Alaskan Malamute\", \"Siberian Husky\",\n \"Dalmatian\", \"Affenpinscher\", \"Basenji\", \"pug\", \"Leonberger\", \"Newfoundland dog\",\n \"Great Pyrenees dog\", \"Samoyed\", \"Pomeranian\", \"Chow Chow\", \"Keeshond\", \"brussels griffon\",\n \"Pembroke Welsh Corgi\", \"Cardigan Welsh Corgi\", \"Toy Poodle\", \"Miniature Poodle\",\n \"Standard Poodle\", \"Mexican hairless dog (xoloitzcuintli)\", \"grey wolf\", \"Alaskan tundra wolf\",\n \"red wolf or maned wolf\", \"coyote\", \"dingo\", \"dhole\", \"African wild dog\", \"hyena\", \"red fox\",\n \"kit fox\", \"Arctic fox\", \"grey fox\", \"tabby cat\", \"tiger cat\", \"Persian cat\", \"Siamese cat\",\n \"Egyptian Mau\", \"cougar\", \"lynx\", \"leopard\", \"snow leopard\", \"jaguar\", \"lion\", \"tiger\",\n \"cheetah\", \"brown bear\", \"American black bear\", \"polar bear\", \"sloth bear\", \"mongoose\",\n \"meerkat\", \"tiger beetle\", \"ladybug\", \"ground beetle\", \"longhorn beetle\", \"leaf beetle\",\n \"dung beetle\", \"rhinoceros beetle\", \"weevil\", \"fly\", \"bee\", \"ant\", \"grasshopper\",\n \"cricket insect\", \"stick insect\", \"cockroach\", \"praying mantis\", \"cicada\", \"leafhopper\",\n \"lacewing\", \"dragonfly\", \"damselfly\", \"red admiral butterfly\", \"ringlet butterfly\",\n \"monarch butterfly\", \"small white butterfly\", \"sulphur butterfly\", \"gossamer-winged butterfly\",\n \"starfish\", \"sea urchin\", \"sea cucumber\", \"cottontail rabbit\", \"hare\", \"Angora rabbit\",\n \"hamster\", \"porcupine\", \"fox squirrel\", \"marmot\", \"beaver\", \"guinea pig\", \"common sorrel horse\",\n \"zebra\", \"pig\", \"wild boar\", \"warthog\", \"hippopotamus\", \"ox\", \"water buffalo\", \"bison\",\n \"ram (adult male sheep)\", \"bighorn sheep\", \"Alpine ibex\", \"hartebeest\", \"impala (antelope)\",\n \"gazelle\", \"arabian camel\", \"llama\", \"weasel\", \"mink\", \"European polecat\",\n \"black-footed ferret\", \"otter\", \"skunk\", \"badger\", \"armadillo\", \"three-toed sloth\", \"orangutan\",\n \"gorilla\", \"chimpanzee\", \"gibbon\", \"siamang\", \"guenon\", \"patas monkey\", \"baboon\", \"macaque\",\n \"langur\", \"black-and-white colobus\", \"proboscis monkey\", \"marmoset\", \"white-headed capuchin\",\n \"howler monkey\", \"titi monkey\", \"Geoffroy's spider monkey\", \"common squirrel monkey\",\n \"ring-tailed lemur\", \"indri\", \"Asian elephant\", \"African bush elephant\", \"red panda\",\n \"giant panda\", \"snoek fish\", \"eel\", \"silver salmon\", \"rock beauty fish\", \"clownfish\",\n \"sturgeon\", \"gar fish\", \"lionfish\", \"pufferfish\", \"abacus\", \"abaya\", \"academic gown\",\n \"accordion\", \"acoustic guitar\", \"aircraft carrier\", \"airliner\", \"airship\", \"altar\", \"ambulance\",\n \"amphibious vehicle\", \"analog clock\", \"apiary\", \"apron\", \"trash can\", \"assault rifle\",\n \"backpack\", \"bakery\", \"balance beam\", \"balloon\", \"ballpoint pen\", \"Band-Aid\", \"banjo\",\n \"baluster / handrail\", \"barbell\", \"barber chair\", \"barbershop\", \"barn\", \"barometer\", \"barrel\",\n \"wheelbarrow\", \"baseball\", \"basketball\", \"bassinet\", \"bassoon\", \"swimming cap\", \"bath towel\",\n \"bathtub\", \"station wagon\", \"lighthouse\", \"beaker\", \"military hat (bearskin or shako)\",\n \"beer bottle\", \"beer glass\", \"bell tower\", \"baby bib\", \"tandem bicycle\", \"bikini\",\n \"ring binder\", \"binoculars\", \"birdhouse\", \"boathouse\", \"bobsleigh\", \"bolo tie\", \"poke bonnet\",\n \"bookcase\", \"bookstore\", \"bottle cap\", \"hunting bow\", \"bow tie\", \"brass memorial plaque\", \"bra\",\n \"breakwater\", \"breastplate\", \"broom\", \"bucket\", \"buckle\", \"bulletproof vest\",\n \"high-speed train\", \"butcher shop\", \"taxicab\", \"cauldron\", \"candle\", \"cannon\", \"canoe\",\n \"can opener\", \"cardigan\", \"car mirror\", \"carousel\", \"tool kit\", \"cardboard box / carton\",\n \"car wheel\", \"automated teller machine\", \"cassette\", \"cassette player\", \"castle\", \"catamaran\",\n \"CD player\", \"cello\", \"mobile phone\", \"chain\", \"chain-link fence\", \"chain mail\", \"chainsaw\",\n \"storage chest\", \"chiffonier\", \"bell or wind chime\", \"china cabinet\", \"Christmas stocking\",\n \"church\", \"movie theater\", \"cleaver\", \"cliff dwelling\", \"cloak\", \"clogs\", \"cocktail shaker\",\n \"coffee mug\", \"coffeemaker\", \"spiral or coil\", \"combination lock\", \"computer keyboard\",\n \"candy store\", \"container ship\", \"convertible\", \"corkscrew\", \"cornet\", \"cowboy boot\",\n \"cowboy hat\", \"cradle\", \"construction crane\", \"crash helmet\", \"crate\", \"infant bed\",\n \"Crock Pot\", \"croquet ball\", \"crutch\", \"cuirass\", \"dam\", \"desk\", \"desktop computer\",\n \"rotary dial telephone\", \"diaper\", \"digital clock\", \"digital watch\", \"dining table\",\n \"dishcloth\", \"dishwasher\", \"disc brake\", \"dock\", \"dog sled\", \"dome\", \"doormat\", \"drilling rig\",\n \"drum\", \"drumstick\", \"dumbbell\", \"Dutch oven\", \"electric fan\", \"electric guitar\",\n \"electric locomotive\", \"entertainment center\", \"envelope\", \"espresso machine\", \"face powder\",\n \"feather boa\", \"filing cabinet\", \"fireboat\", \"fire truck\", \"fire screen\", \"flagpole\", \"flute\",\n \"folding chair\", \"football helmet\", \"forklift\", \"fountain\", \"fountain pen\", \"four-poster bed\",\n \"freight car\", \"French horn\", \"frying pan\", \"fur coat\", \"garbage truck\",\n \"gas mask or respirator\", \"gas pump\", \"goblet\", \"go-kart\", \"golf ball\", \"golf cart\", \"gondola\",\n \"gong\", \"gown\", \"grand piano\", \"greenhouse\", \"radiator grille\", \"grocery store\", \"guillotine\",\n \"hair clip\", \"hair spray\", \"half-track\", \"hammer\", \"hamper\", \"hair dryer\", \"hand-held computer\",\n \"handkerchief\", \"hard disk drive\", \"harmonica\", \"harp\", \"combine harvester\", \"hatchet\",\n \"holster\", \"home theater\", \"honeycomb\", \"hook\", \"hoop skirt\", \"gymnastic horizontal bar\",\n \"horse-drawn vehicle\", \"hourglass\", \"iPod\", \"clothes iron\", \"carved pumpkin\", \"jeans\", \"jeep\",\n \"T-shirt\", \"jigsaw puzzle\", \"rickshaw\", \"joystick\", \"kimono\", \"knee pad\", \"knot\", \"lab coat\",\n \"ladle\", \"lampshade\", \"laptop computer\", \"lawn mower\", \"lens cap\", \"letter opener\", \"library\",\n \"lifeboat\", \"lighter\", \"limousine\", \"ocean liner\", \"lipstick\", \"slip-on shoe\", \"lotion\",\n \"music speaker\", \"loupe magnifying glass\", \"sawmill\", \"magnetic compass\", \"messenger bag\",\n \"mailbox\", \"tights\", \"one-piece bathing suit\", \"manhole cover\", \"maraca\", \"marimba\", \"mask\",\n \"matchstick\", \"maypole\", \"maze\", \"measuring cup\", \"medicine cabinet\", \"megalith\", \"microphone\",\n \"microwave oven\", \"military uniform\", \"milk can\", \"minibus\", \"miniskirt\", \"minivan\", \"missile\",\n \"mitten\", \"mixing bowl\", \"mobile home\", \"ford model t\", \"modem\", \"monastery\", \"monitor\",\n \"moped\", \"mortar and pestle\", \"graduation cap\", \"mosque\", \"mosquito net\", \"vespa\",\n \"mountain bike\", \"tent\", \"computer mouse\", \"mousetrap\", \"moving van\", \"muzzle\", \"metal nail\",\n \"neck brace\", \"necklace\", \"baby pacifier\", \"notebook computer\", \"obelisk\", \"oboe\", \"ocarina\",\n \"odometer\", \"oil filter\", \"pipe organ\", \"oscilloscope\", \"overskirt\", \"bullock cart\",\n \"oxygen mask\", \"product packet / packaging\", \"paddle\", \"paddle wheel\", \"padlock\", \"paintbrush\",\n \"pajamas\", \"palace\", \"pan flute\", \"paper towel\", \"parachute\", \"parallel bars\", \"park bench\",\n \"parking meter\", \"railroad car\", \"patio\", \"payphone\", \"pedestal\", \"pencil case\",\n \"pencil sharpener\", \"perfume\", \"Petri dish\", \"photocopier\", \"plectrum\", \"Pickelhaube\",\n \"picket fence\", \"pickup truck\", \"pier\", \"piggy bank\", \"pill bottle\", \"pillow\", \"ping-pong ball\",\n \"pinwheel\", \"pirate ship\", \"drink pitcher\", \"block plane\", \"planetarium\", \"plastic bag\",\n \"plate rack\", \"farm plow\", \"plunger\", \"Polaroid camera\", \"pole\", \"police van\", \"poncho\",\n \"pool table\", \"soda bottle\", \"plant pot\", \"potter's wheel\", \"power drill\", \"prayer rug\",\n \"printer\", \"prison\", \"missile\", \"projector\", \"hockey puck\", \"punching bag\", \"purse\", \"quill\",\n \"quilt\", \"race car\", \"racket\", \"radiator\", \"radio\", \"radio telescope\", \"rain barrel\",\n \"recreational vehicle\", \"fishing casting reel\", \"reflex camera\", \"refrigerator\",\n \"remote control\", \"restaurant\", \"revolver\", \"rifle\", \"rocking chair\", \"rotisserie\", \"eraser\",\n \"rugby ball\", \"ruler measuring stick\", \"sneaker\", \"safe\", \"safety pin\", \"salt shaker\", \"sandal\",\n \"sarong\", \"saxophone\", \"scabbard\", \"weighing scale\", \"school bus\", \"schooner\", \"scoreboard\",\n \"CRT monitor\", \"screw\", \"screwdriver\", \"seat belt\", \"sewing machine\", \"shield\", \"shoe store\",\n \"shoji screen / room divider\", \"shopping basket\", \"shopping cart\", \"shovel\", \"shower cap\",\n \"shower curtain\", \"ski\", \"balaclava ski mask\", \"sleeping bag\", \"slide rule\", \"sliding door\",\n \"slot machine\", \"snorkel\", \"snowmobile\", \"snowplow\", \"soap dispenser\", \"soccer ball\", \"sock\",\n \"solar thermal collector\", \"sombrero\", \"soup bowl\", \"keyboard space bar\", \"space heater\",\n \"space shuttle\", \"spatula\", \"motorboat\", \"spider web\", \"spindle\", \"sports car\", \"spotlight\",\n \"stage\", \"steam locomotive\", \"through arch bridge\", \"steel drum\", \"stethoscope\", \"scarf\",\n \"stone wall\", \"stopwatch\", \"stove\", \"strainer\", \"tram\", \"stretcher\", \"couch\", \"stupa\",\n \"submarine\", \"suit\", \"sundial\", \"sunglasses\", \"sunglasses\", \"sunscreen\", \"suspension bridge\",\n \"mop\", \"sweatshirt\", \"swim trunks / shorts\", \"swing\", \"electrical switch\", \"syringe\",\n \"table lamp\", \"tank\", \"tape player\", \"teapot\", \"teddy bear\", \"television\", \"tennis ball\",\n \"thatched roof\", \"front curtain\", \"thimble\", \"threshing machine\", \"throne\", \"tile roof\",\n \"toaster\", \"tobacco shop\", \"toilet seat\", \"torch\", \"totem pole\", \"tow truck\", \"toy store\",\n \"tractor\", \"semi-trailer truck\", \"tray\", \"trench coat\", \"tricycle\", \"trimaran\", \"tripod\",\n \"triumphal arch\", \"trolleybus\", \"trombone\", \"hot tub\", \"turnstile\", \"typewriter keyboard\",\n \"umbrella\", \"unicycle\", \"upright piano\", \"vacuum cleaner\", \"vase\", \"vaulted or arched ceiling\",\n \"velvet fabric\", \"vending machine\", \"vestment\", \"viaduct\", \"violin\", \"volleyball\",\n \"waffle iron\", \"wall clock\", \"wallet\", \"wardrobe\", \"military aircraft\", \"sink\",\n \"washing machine\", \"water bottle\", \"water jug\", \"water tower\", \"whiskey jug\", \"whistle\",\n \"hair wig\", \"window screen\", \"window shade\", \"Windsor tie\", \"wine bottle\", \"airplane wing\",\n \"wok\", \"wooden spoon\", \"wool\", \"split-rail fence\", \"shipwreck\", \"sailboat\", \"yurt\", \"website\",\n \"comic book\", \"crossword\", \"traffic or street sign\", \"traffic light\", \"dust jacket\", \"menu\",\n \"plate\", \"guacamole\", \"consomme\", \"hot pot\", \"trifle\", \"ice cream\", \"popsicle\", \"baguette\",\n \"bagel\", \"pretzel\", \"cheeseburger\", \"hot dog\", \"mashed potatoes\", \"cabbage\", \"broccoli\",\n \"cauliflower\", \"zucchini\", \"spaghetti squash\", \"acorn squash\", \"butternut squash\", \"cucumber\",\n \"artichoke\", \"bell pepper\", \"cardoon\", \"mushroom\", \"Granny Smith apple\", \"strawberry\", \"orange\",\n \"lemon\", \"fig\", \"pineapple\", \"banana\", \"jackfruit\", \"cherimoya (custard apple)\", \"pomegranate\",\n \"hay\", \"carbonara\", \"chocolate syrup\", \"dough\", \"meatloaf\", \"pizza\", \"pot pie\", \"burrito\",\n \"red wine\", \"espresso\", \"tea cup\", \"eggnog\", \"mountain\", \"bubble\", \"cliff\", \"coral reef\",\n \"geyser\", \"lakeshore\", \"promontory\", \"sandbar\", \"beach\", \"valley\", \"volcano\", \"baseball player\",\n \"bridegroom\", \"scuba diver\", \"rapeseed\", \"daisy\", \"yellow lady's slipper\", \"corn\", \"acorn\",\n \"rose hip\", \"horse chestnut seed\", \"coral fungus\", \"agaric\", \"gyromitra\", \"stinkhorn mushroom\",\n \"earth star fungus\", \"hen of the woods mushroom\", \"bolete\", \"corn cob\", \"toilet paper\"]\n\n\n\n\n\nopenai_imagenet_template = [\n lambda c: f'a photo of {c}.',\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]\n"
},
{
"path": "src/training/logger.py",
"content": "import logging\n\n\ndef setup_logging(log_file, level, include_host=False):\n if include_host:\n import socket\n hostname = socket.gethostname()\n formatter = logging.Formatter(\n f'%(asctime)s | {hostname} | %(levelname)s | %(message)s', datefmt='%Y-%m-%d,%H:%M:%S')\n else:\n formatter = logging.Formatter('%(asctime)s | %(levelname)s | %(message)s', datefmt='%Y-%m-%d,%H:%M:%S')\n\n logging.root.setLevel(level)\n loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]\n for logger in loggers:\n logger.setLevel(level)\n\n stream_handler = logging.StreamHandler()\n stream_handler.setFormatter(formatter)\n logging.root.addHandler(stream_handler)\n\n if log_file:\n file_handler = logging.FileHandler(filename=log_file)\n file_handler.setFormatter(formatter)\n logging.root.addHandler(file_handler)\n\n"
},
{
"path": "src/training/main.py",
"content": "import glob\nimport logging\nimport os\nimport re\nimport subprocess\nimport sys\nimport random\nfrom datetime import datetime\n\nimport numpy as np\nimport torch\nfrom torch import optim\nfrom torch.cuda.amp import GradScaler\n\ntry:\n import wandb\nexcept ImportError:\n wandb = None\n\ntry:\n import torch.utils.tensorboard as tensorboard\nexcept ImportError:\n tensorboard = None\n\ntry:\n import horovod.torch as hvd\nexcept ImportError:\n hvd = None\n\nfrom ..open_clip import create_model_and_transforms, trace_model, get_tokenizer, create_loss\nfrom ..training.data import get_data\nfrom ..training.distributed import is_master, init_distributed_device, broadcast_object\nfrom ..training.logger import setup_logging\nfrom ..training.params import parse_args\nfrom ..training.scheduler import cosine_lr, const_lr, const_lr_cooldown\nfrom ..training.train import train_one_epoch, evaluate\nfrom ..training.file_utils import pt_load, check_exists, start_sync_process, remote_sync\n\n\nLATEST_CHECKPOINT_NAME = \"epoch_latest.pt\"\n\n\ndef random_seed(seed=42, rank=0):\n torch.manual_seed(seed + rank)\n np.random.seed(seed + rank)\n random.seed(seed + rank)\n os.environ['PYTHONHASHSEED'] = str(seed + rank)\n torch.cuda.manual_seed(seed + rank)\n torch.cuda.manual_seed_all(seed + rank)\n torch.backends.cudnn.deterministic = True\n\n\ndef natural_key(string_):\n \"\"\"See http://www.codinghorror.com/blog/archives/001018.html\"\"\"\n return [int(s) if s.isdigit() else s for s in re.split(r'(\\d+)', string_.lower())]\n\n\ndef get_latest_checkpoint(path: str, remote : bool):\n # as writen, this glob recurses, so can pick up checkpoints across multiple sub-folders\n if remote:\n result = subprocess.run([\"aws\", \"s3\", \"ls\", path + \"/\"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n print(result)\n if result.returncode == 1:\n return None\n checkpoints = [os.path.join(path, x.split(' ')[-1]) for x in result.stdout.decode().split('\\n')[:-1]]\n else:\n checkpoints = glob.glob(path + '**/*.pt', recursive=True)\n if checkpoints:\n checkpoints = sorted(checkpoints, key=natural_key)\n return checkpoints[-1]\n return None\n\n\ndef main(args):\n args = parse_args(args)\n\n if torch.cuda.is_available():\n # This enables tf32 on Ampere GPUs which is only 8% slower than\n # float16 and almost as accurate as float32\n # This was a default in pytorch until 1.12\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.benchmark = True\n torch.backends.cudnn.deterministic = True\n\n # fully initialize distributed device environment\n device = init_distributed_device(args)\n\n # get the name of the experiments\n if args.name is None:\n # sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?\n model_name_safe = args.model.replace('/', '-')\n date_str = datetime.now().strftime(\"%Y_%m_%d-%H_%M_%S\")\n if args.distributed:\n # sync date_str from master to all ranks\n date_str = broadcast_object(args, date_str)\n args.name = '-'.join([\n date_str,\n f\"model_{model_name_safe}\",\n f\"lr_{args.lr}\",\n f\"b_{args.batch_size}\",\n f\"j_{args.workers}\",\n f\"p_{args.precision}\",\n ])\n\n resume_latest = args.resume == 'latest'\n log_base_path = os.path.join(args.logs, args.name)\n args.log_path = None\n if is_master(args, local=args.log_local):\n os.makedirs(log_base_path, exist_ok=True)\n log_filename = f'out-{args.rank}' if args.log_local else 'out.log'\n args.log_path = os.path.join(log_base_path, log_filename)\n if os.path.exists(args.log_path) and not resume_latest:\n print(\n \"Error. Experiment already exists. Use --name {} to specify a new experiment.\"\n )\n return -1\n\n # Setup text logger\n args.log_level = logging.DEBUG if args.debug else logging.INFO\n setup_logging(args.log_path, args.log_level)\n\n # Setup wandb, tensorboard, checkpoint logging\n args.wandb = 'wandb' in args.report_to or 'all' in args.report_to\n args.tensorboard = 'tensorboard' in args.report_to or 'all' in args.report_to\n args.checkpoint_path = os.path.join(log_base_path, \"checkpoints\")\n if is_master(args):\n args.tensorboard_path = os.path.join(log_base_path, \"tensorboard\") if args.tensorboard else ''\n for dirname in [args.tensorboard_path, args.checkpoint_path]:\n if dirname:\n os.makedirs(dirname, exist_ok=True)\n else:\n args.tensorboard_path = ''\n\n if resume_latest:\n resume_from = None\n checkpoint_path = args.checkpoint_path\n # If using remote_sync, need to check the remote instead of the local checkpoints folder.\n if args.remote_sync is not None:\n checkpoint_path = os.path.join(args.remote_sync, args.name, \"checkpoints\")\n if args.save_most_recent:\n print('Error. Cannot use save-most-recent with remote_sync and resume latest.')\n return -1\n if args.remote_sync_protocol != 's3':\n print('Error. Sync protocol not supported when using resume latest.')\n return -1\n if is_master(args):\n # Checking for existing checkpoint via master rank only. It is possible for\n # different rank processes to see different files if a shared file-system is under\n # stress, however it's very difficult to fully work around such situations.\n if args.save_most_recent:\n # if --save-most-recent flag is set, look for latest at a fixed filename\n resume_from = os.path.join(checkpoint_path, LATEST_CHECKPOINT_NAME)\n if not os.path.exists(resume_from):\n # If no latest checkpoint has been saved yet, don't try to resume\n resume_from = None\n else:\n # otherwise, list checkpoint dir contents and pick the newest checkpoint\n resume_from = get_latest_checkpoint(checkpoint_path, remote=args.remote_sync is not None)\n if resume_from:\n logging.info(f'Found latest resume checkpoint at {resume_from}.')\n else:\n logging.info(f'No latest resume checkpoint found in {checkpoint_path}.')\n if args.distributed:\n # sync found checkpoint path to all ranks\n resume_from = broadcast_object(args, resume_from)\n args.resume = resume_from\n\n if args.copy_codebase:\n copy_codebase(args)\n\n # start the sync proces if remote-sync is not None\n remote_sync_process = None\n if is_master(args) and args.remote_sync is not None:\n # first make sure it works\n result = remote_sync(\n os.path.join(args.logs, args.name), \n os.path.join(args.remote_sync, args.name), \n args.remote_sync_protocol\n )\n if result:\n logging.info('remote sync successful.')\n else:\n logging.info('Error: remote sync failed. Exiting.')\n return -1\n # if all looks good, start a process to do this every args.remote_sync_frequency seconds\n remote_sync_process = start_sync_process(\n args.remote_sync_frequency,\n os.path.join(args.logs, args.name), \n os.path.join(args.remote_sync, args.name), \n args.remote_sync_protocol\n )\n remote_sync_process.start()\n\n if args.precision == 'fp16':\n logging.warning(\n 'It is recommended to use AMP mixed-precision instead of FP16. '\n 'FP16 support needs further verification and tuning, especially for train.')\n\n if args.horovod:\n logging.info(\n f'Running in horovod mode with multiple processes / nodes. Device: {args.device}.'\n f'Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}.')\n elif args.distributed:\n logging.info(\n f'Running in distributed mode with multiple processes. Device: {args.device}.'\n f'Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}.')\n else:\n logging.info(f'Running with a single process. Device {args.device}.')\n\n dist_model = None\n args.distill = args.distill_model is not None and args.distill_pretrained is not None\n if args.distill:\n #FIXME: support distillation with grad accum.\n assert args.accum_freq == 1\n #FIXME: support distillation with coca.\n assert 'coca' not in args.model.lower()\n\n if isinstance(args.force_image_size, (tuple, list)) and len(args.force_image_size) == 1:\n # arg is nargs, single (square) image size list -> int\n args.force_image_size = args.force_image_size[0]\n random_seed(args.seed, 0)\n model, preprocess_train, preprocess_val = create_model_and_transforms(\n args.model,\n args.pretrained,\n precision=args.precision,\n device=device,\n jit=args.torchscript,\n force_quick_gelu=args.force_quick_gelu,\n force_custom_text=args.force_custom_text,\n force_patch_dropout=args.force_patch_dropout,\n force_image_size=args.force_image_size,\n pretrained_image=args.pretrained_image,\n image_mean=args.image_mean,\n image_std=args.image_std,\n aug_cfg=args.aug_cfg,\n output_dict=True,\n )\n if args.distill:\n # FIXME: currenlty assumes the model your distilling from has the same tokenizer & transforms.\n dist_model, _, _ = create_model_and_transforms(\n args.distill_model, \n args.distill_pretrained,\n device=device,\n precision=args.precision,\n output_dict=True,\n )\n\n random_seed(args.seed, args.rank)\n\n if args.trace:\n model = trace_model(model, batch_size=args.batch_size, device=device)\n\n if args.lock_image:\n # lock image tower as per LiT - https://arxiv.org/abs/2111.07991\n model.lock_image_tower(\n unlocked_groups=args.lock_image_unlocked_groups,\n freeze_bn_stats=args.lock_image_freeze_bn_stats)\n if args.lock_text:\n model.lock_text_tower(\n unlocked_layers=args.lock_text_unlocked_layers,\n freeze_layer_norm=args.lock_text_freeze_layer_norm)\n\n if args.grad_checkpointing:\n model.set_grad_checkpointing()\n\n if is_master(args):\n logging.info(\"Model:\")\n logging.info(f\"{str(model)}\")\n logging.info(\"Params:\")\n params_file = os.path.join(args.logs, args.name, \"params.txt\")\n with open(params_file, \"w\") as f:\n for name in sorted(vars(args)):\n val = getattr(args, name)\n logging.info(f\" {name}: {val}\")\n f.write(f\"{name}: {val}\\n\")\n\n if args.distributed and not args.horovod:\n if args.use_bn_sync:\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)\n ddp_args = {}\n if args.ddp_static_graph:\n # this doesn't exist in older PyTorch, arg only added if enabled\n ddp_args['static_graph'] = True\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[device], **ddp_args)\n \n if args.distill:\n dist_model = torch.nn.parallel.DistributedDataParallel(dist_model, device_ids=[device], **ddp_args)\n\n # create optimizer and scaler\n optimizer = None\n scaler = None\n\n if args.train_data or args.dataset_type == \"synthetic\":\n assert not args.trace, 'Cannot train with traced model'\n\n exclude = lambda n, p: p.ndim < 2 or \"bn\" in n or \"ln\" in n or \"bias\" in n or 'logit_scale' in n\n include = lambda n, p: not exclude(n, p)\n\n named_parameters = list(model.named_parameters())\n gain_or_bias_params = [p for n, p in named_parameters if exclude(n, p) and p.requires_grad]\n rest_params = [p for n, p in named_parameters if include(n, p) and p.requires_grad]\n\n optimizer = optim.AdamW(\n [\n {\"params\": gain_or_bias_params, \"weight_decay\": 0.},\n {\"params\": rest_params, \"weight_decay\": args.wd},\n ],\n lr=args.lr,\n betas=(args.beta1, args.beta2),\n eps=args.eps,\n )\n if args.horovod:\n optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())\n hvd.broadcast_parameters(model.state_dict(), root_rank=0)\n hvd.broadcast_optimizer_state(optimizer, root_rank=0)\n\n scaler = GradScaler() if args.precision == \"amp\" else None\n\n # optionally resume from a checkpoint\n start_epoch = 0\n if args.resume is not None:\n checkpoint = pt_load(args.resume, map_location='cpu')\n if 'epoch' in checkpoint:\n # resuming a train checkpoint w/ epoch and optimizer state\n start_epoch = checkpoint[\"epoch\"]\n sd = checkpoint[\"state_dict\"]\n if not args.distributed and next(iter(sd.items()))[0].startswith('module'):\n sd = {k[len('module.'):]: v for k, v in sd.items()}\n model.load_state_dict(sd)\n if optimizer is not None:\n optimizer.load_state_dict(checkpoint[\"optimizer\"])\n if scaler is not None and 'scaler' in checkpoint:\n scaler.load_state_dict(checkpoint['scaler'])\n logging.info(f\"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})\")\n else:\n # loading a bare (model only) checkpoint for fine-tune or evaluation\n model.load_state_dict(checkpoint)\n logging.info(f\"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})\")\n\n # initialize datasets\n data = get_data(args, (preprocess_train, preprocess_val), epoch=start_epoch, tokenizer=get_tokenizer(args.model))\n assert len(data), 'At least one train or eval dataset must be specified.'\n\n # create scheduler if train\n scheduler = None\n if 'train' in data and optimizer is not None:\n total_steps = (data[\"train\"].dataloader.num_batches // args.accum_freq) * args.epochs\n if args.lr_scheduler == \"cosine\":\n scheduler = cosine_lr(optimizer, args.lr, args.warmup, total_steps)\n elif args.lr_scheduler == \"const\":\n scheduler = const_lr(optimizer, args.lr, args.warmup, total_steps)\n elif args.lr_scheduler == \"const-cooldown\":\n assert args.epochs_cooldown is not None,\\\n \"Please specify the number of cooldown epochs for this lr schedule.\"\n cooldown_steps = (data[\"train\"].dataloader.num_batches // args.accum_freq) * args.epochs_cooldown\n scheduler = const_lr_cooldown(\n optimizer, args.lr, args.warmup, total_steps,\n cooldown_steps, args.lr_cooldown_power, args.lr_cooldown_end)\n else:\n logging.error(\n f'Unknown scheduler, {args.lr_scheduler}. Available options are: cosine, const, const-cooldown.')\n exit(1)\n\n # determine if this worker should save logs and checkpoints. only do so if it is rank == 0\n args.save_logs = args.logs and args.logs.lower() != 'none' and is_master(args)\n writer = None\n if args.save_logs and args.tensorboard:\n assert tensorboard is not None, \"Please install tensorboard.\"\n writer = tensorboard.SummaryWriter(args.tensorboard_path)\n\n if args.wandb and is_master(args):\n assert wandb is not None, 'Please install wandb.'\n logging.debug('Starting wandb.')\n args.train_sz = data[\"train\"].dataloader.num_samples\n if args.val_data is not None:\n args.val_sz = data[\"val\"].dataloader.num_samples\n # you will have to configure this for your project!\n wandb.init(\n project=args.wandb_project_name,\n dir=args.logs,\n name=args.name,\n id=args.name,\n notes=args.wandb_notes,\n tags=[],\n resume='auto' if args.resume == \"latest\" else None,\n config=vars(args),\n )\n if args.debug:\n wandb.watch(model, log='all')\n wandb.save(params_file)\n logging.debug('Finished loading wandb.')\n\n if 'train' not in data:\n evaluate(model, data, start_epoch, args, writer)\n return\n\n loss = create_loss(args)\n\n for epoch in range(start_epoch, args.epochs):\n if is_master(args):\n logging.info(f'Start epoch {epoch}')\n\n train_one_epoch(model, data, loss, epoch, optimizer, scaler, scheduler, dist_model, args, tb_writer=writer)\n completed_epoch = epoch + 1\n\n if any(v in data for v in ('val', 'imagenet-val', 'imagenet-v2')):\n evaluate(model, data, completed_epoch, args, writer)\n\n # Saving checkpoints.\n if args.save_logs:\n checkpoint_dict = {\n \"epoch\": completed_epoch,\n \"name\": args.name,\n \"state_dict\": model.state_dict(),\n \"optimizer\": optimizer.state_dict(),\n }\n if scaler is not None:\n checkpoint_dict[\"scaler\"] = scaler.state_dict()\n\n if completed_epoch == args.epochs or (\n args.save_frequency > 0 and (completed_epoch % args.save_frequency) == 0\n ):\n torch.save(\n checkpoint_dict,\n os.path.join(args.checkpoint_path, f\"epoch_{completed_epoch}.pt\"),\n )\n if args.delete_previous_checkpoint:\n previous_checkpoint = os.path.join(args.checkpoint_path, f\"epoch_{completed_epoch - 1}.pt\")\n if os.path.exists(previous_checkpoint):\n os.remove(previous_checkpoint)\n\n if args.save_most_recent:\n # try not to corrupt the latest checkpoint if save fails\n tmp_save_path = os.path.join(args.checkpoint_path, \"tmp.pt\")\n latest_save_path = os.path.join(args.checkpoint_path, LATEST_CHECKPOINT_NAME)\n torch.save(checkpoint_dict, tmp_save_path)\n os.replace(tmp_save_path, latest_save_path)\n\n if args.wandb and is_master(args):\n wandb.finish()\n\n # run a final sync.\n if remote_sync_process is not None:\n logging.info('Final remote sync.')\n remote_sync_process.terminate()\n result = remote_sync(\n os.path.join(args.logs, args.name), \n os.path.join(args.remote_sync, args.name), \n args.remote_sync_protocol\n )\n if result:\n logging.info('Final remote sync successful.')\n else:\n logging.info('Final remote sync failed.')\n \n\ndef copy_codebase(args):\n from shutil import copytree, ignore_patterns\n new_code_path = os.path.join(args.logs, args.name, \"code\")\n if os.path.exists(new_code_path):\n print(\n f\"Error. Experiment already exists at {new_code_path}. Use --name to specify a new experiment.\"\n )\n return -1\n print(f\"Copying codebase to {new_code_path}\")\n current_code_path = os.path.realpath(__file__)\n for _ in range(3):\n current_code_path = os.path.dirname(current_code_path)\n copytree(current_code_path, new_code_path, ignore=ignore_patterns('log', 'logs', 'wandb'))\n print(\"Done copying code.\")\n return 1\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n"
},
{
"path": "src/training/params.py",
"content": "import argparse\nimport ast\n\n\ndef get_default_params(model_name):\n # Params from paper (https://arxiv.org/pdf/2103.00020.pdf)\n model_name = model_name.lower()\n if \"vit\" in model_name:\n return {\"lr\": 5.0e-4, \"beta1\": 0.9, \"beta2\": 0.98, \"eps\": 1.0e-6}\n else:\n return {\"lr\": 5.0e-4, \"beta1\": 0.9, \"beta2\": 0.999, \"eps\": 1.0e-8}\n\n\nclass ParseKwargs(argparse.Action):\n def __call__(self, parser, namespace, values, option_string=None):\n kw = {}\n for value in values:\n key, value = value.split('=')\n try:\n kw[key] = ast.literal_eval(value)\n except ValueError:\n kw[key] = str(value) # fallback to string (avoid need to escape on command line)\n setattr(namespace, self.dest, kw)\n\n\ndef parse_args(args):\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--train-data\",\n type=str,\n default=None,\n help=\"Path to file(s) with training data. When using webdataset, multiple datasources can be combined using the `::` separator.\",\n )\n parser.add_argument(\n \"--train-data-upsampling-factors\",\n type=str,\n default=None,\n help=(\n \"When using multiple data sources with webdataset and sampling with replacement, this can be used to upsample specific data sources. \"\n \"Similar to --train-data, this should be a string with as many numbers as there are data sources, separated by `::` (e.g. 1::2::0.5) \"\n \"By default, datapoints are sampled uniformly regardless of the dataset sizes.\"\n )\n )\n parser.add_argument(\n \"--val-data\",\n type=str,\n default=None,\n help=\"Path to file(s) with validation data\",\n )\n parser.add_argument(\n \"--train-num-samples\",\n type=int,\n default=None,\n help=\"Number of samples in dataset. Required for webdataset if not available in info file.\",\n )\n parser.add_argument(\n \"--val-num-samples\",\n type=int,\n default=None,\n help=\"Number of samples in dataset. Useful for webdataset if not available in info file.\",\n )\n parser.add_argument(\n \"--dataset-type\",\n choices=[\"webdataset\", \"csv\", \"synthetic\", \"auto\"],\n default=\"auto\",\n help=\"Which type of dataset to process.\"\n )\n parser.add_argument(\n \"--dataset-resampled\",\n default=False,\n action=\"store_true\",\n help=\"Whether to use sampling with replacement for webdataset shard selection.\"\n )\n parser.add_argument(\n \"--csv-separator\",\n type=str,\n default=\"\\t\",\n help=\"For csv-like datasets, which separator to use.\"\n )\n parser.add_argument(\n \"--csv-img-key\",\n type=str,\n default=\"filepath\",\n help=\"For csv-like datasets, the name of the key for the image paths.\"\n )\n parser.add_argument(\n \"--csv-caption-key\",\n type=str,\n default=\"title\",\n help=\"For csv-like datasets, the name of the key for the captions.\"\n )\n parser.add_argument(\n \"--imagenet-val\",\n type=str,\n default=None,\n help=\"Path to imagenet val set for conducting zero shot evaluation.\",\n )\n parser.add_argument(\n \"--imagenet-v2\",\n type=str,\n default=None,\n help=\"Path to imagenet v2 for conducting zero shot evaluation.\",\n )\n parser.add_argument(\n \"--logs\",\n type=str,\n default=\"../storage/model\",\n help=\"Where to store tensorboard logs. Use None to avoid storing logs.\",\n )\n parser.add_argument(\n \"--log-local\",\n action=\"store_true\",\n default=False,\n help=\"log files on local master, otherwise global master only.\",\n )\n parser.add_argument(\n \"--name\",\n type=str,\n default=None,\n help=\"Optional identifier for the experiment when storing logs. Otherwise use current time.\",\n )\n parser.add_argument(\n \"--workers\", type=int, default=8, help=\"Number of dataloader workers per GPU.\"\n )\n parser.add_argument(\n \"--batch-size\", type=int, default=4096, help=\"Batch size per GPU.\"\n )\n parser.add_argument(\n \"--epochs\", type=int, default=100, help=\"Number of epochs to train for.\"\n )\n parser.add_argument(\n \"--epochs-cooldown\", type=int, default=None,\n help=\"When scheduler w/ cooldown used, perform cooldown from total_epochs - cooldown_epochs onwards.\"\n )\n parser.add_argument(\"--lr\", type=float, default=1e-4, help=\"Learning rate.\")\n parser.add_argument(\"--beta1\", type=float, default=None, help=\"Adam beta 1.\")\n parser.add_argument(\"--beta2\", type=float, default=None, help=\"Adam beta 2.\")\n parser.add_argument(\"--eps\", type=float, default=None, help=\"Adam epsilon.\")\n parser.add_argument(\"--wd\", type=float, default=0.2, help=\"Weight decay.\")\n parser.add_argument(\n \"--warmup\", type=int, default=1000, help=\"Number of steps to warmup for.\"\n )\n parser.add_argument(\n \"--use-bn-sync\",\n default=False,\n action=\"store_true\",\n help=\"Whether to use batch norm sync.\")\n parser.add_argument(\n \"--skip-scheduler\",\n action=\"store_true\",\n default=False,\n help=\"Use this flag to skip the learning rate decay.\",\n )\n parser.add_argument(\n \"--lr-scheduler\",\n type=str,\n default='cosine',\n help=\"LR scheduler. One of: 'cosine', 'const' (constant), 'const-cooldown' (constant w/ cooldown). Default: cosine\",\n )\n parser.add_argument(\n \"--lr-cooldown-end\", type=float, default=0.0,\n help=\"End learning rate for cooldown schedule. Default: 0\"\n )\n parser.add_argument(\n \"--lr-cooldown-power\", type=float, default=1.0,\n help=\"Power for polynomial cooldown schedule. Default: 1.0 (linear decay)\"\n )\n parser.add_argument(\n \"--save-frequency\", type=int, default=1, help=\"How often to save checkpoints.\"\n )\n parser.add_argument(\n \"--save-most-recent\",\n action=\"store_true\",\n default=False,\n help=\"Always save the most recent model trained to epoch_latest.pt.\",\n )\n parser.add_argument(\n \"--zeroshot-frequency\", type=int, default=2, help=\"How often to run zero shot.\"\n )\n parser.add_argument(\n \"--val-frequency\", type=int, default=1, help=\"How often to run evaluation with val data.\"\n )\n parser.add_argument(\n \"--resume\",\n default=None,\n type=str,\n help=\"path to latest checkpoint (default: none)\",\n )\n parser.add_argument(\n \"--precision\",\n choices=[\"amp\", \"amp_bf16\", \"amp_bfloat16\", \"bf16\", \"fp16\", \"fp32\"],\n default=\"amp\",\n help=\"Floating point precision.\"\n )\n parser.add_argument(\n \"--model\",\n type=str,\n default=\"ViT-B-16\",\n help=\"Name of the vision backbone to use.\",\n )\n parser.add_argument(\n \"--pretrained\",\n default=None,\n type=str,\n help=\"Use a pretrained CLIP model weights with the specified tag or file path.\",\n )\n parser.add_argument(\n \"--pretrained-image\",\n default=False,\n action='store_true',\n help=\"Load imagenet pretrained weights for image tower backbone if available.\",\n )\n parser.add_argument(\n \"--lock-image\",\n default=False,\n action='store_true',\n help=\"Lock full image tower by disabling gradients.\",\n )\n parser.add_argument(\n \"--lock-image-unlocked-groups\",\n type=int,\n default=0,\n help=\"Leave last n image tower layer groups unlocked.\",\n )\n parser.add_argument(\n \"--lock-image-freeze-bn-stats\",\n default=False,\n action='store_true',\n help=\"Freeze BatchNorm running stats in image tower for any locked layers.\",\n )\n parser.add_argument(\n '--image-mean', type=float, nargs='+', default=[0.485, 0.456, 0.406], metavar='MEAN',\n help='Override default image mean value of dataset')\n parser.add_argument(\n '--image-std', type=float, nargs='+', default=[0.229, 0.224, 0.225], metavar='STD',\n help='Override default image std deviation of of dataset')\n parser.add_argument('--aug-cfg', nargs='*', default={}, action=ParseKwargs)\n parser.add_argument(\n \"--grad-checkpointing\",\n default=False,\n action='store_true',\n help=\"Enable gradient checkpointing.\",\n )\n parser.add_argument(\n \"--local-loss\",\n default=False,\n action=\"store_true\",\n help=\"calculate loss w/ local features @ global (instead of realizing full global @ global matrix)\"\n )\n parser.add_argument(\n \"--gather-with-grad\",\n default=False,\n action=\"store_true\",\n help=\"enable full distributed gradient for feature gather\"\n )\n parser.add_argument(\n '--force-image-size', type=int, nargs='+', default=None,\n help='Override default image size'\n )\n parser.add_argument(\n \"--force-quick-gelu\",\n default=False,\n action='store_true',\n help=\"Force use of QuickGELU activation for non-OpenAI transformer models.\",\n )\n parser.add_argument(\n \"--force-patch-dropout\",\n default=None,\n type=float,\n help=\"Override the patch dropout during training, for fine tuning with no dropout near the end as in the paper\",\n )\n parser.add_argument(\n \"--force-custom-text\",\n default=False,\n action='store_true',\n help=\"Force use of CustomTextCLIP model (separate text-tower).\",\n )\n parser.add_argument(\n \"--torchscript\",\n default=False,\n action='store_true',\n help=\"torch.jit.script the model, also uses jit version of OpenAI models if pretrained=='openai'\",\n )\n parser.add_argument(\n \"--trace\",\n default=False,\n action='store_true',\n help=\"torch.jit.trace the model for inference / eval only\",\n )\n parser.add_argument(\n \"--accum-freq\", type=int, default=1, help=\"Update the model every --acum-freq steps.\"\n )\n # arguments for distributed training\n parser.add_argument(\n \"--dist-url\",\n default=\"env://\",\n type=str,\n help=\"url used to set up distributed training\",\n )\n parser.add_argument(\n \"--dist-backend\", default=\"nccl\", type=str, help=\"distributed backend\"\n )\n parser.add_argument(\n \"--report-to\",\n default='tensorboard',\n type=str,\n help=\"Options are ['wandb', 'tensorboard', 'wandb,tensorboard']\"\n )\n parser.add_argument(\n \"--wandb-notes\",\n default='',\n type=str,\n help=\"Notes if logging with wandb\"\n )\n parser.add_argument(\n \"--wandb-project-name\",\n type=str,\n default='open-clip',\n help=\"Name of the project if logging with wandb.\",\n )\n parser.add_argument(\n \"--debug\",\n default=False,\n action=\"store_true\",\n help=\"If true, more information is logged.\"\n )\n parser.add_argument(\n \"--copy-codebase\",\n default=False,\n action=\"store_true\",\n help=\"If true, we copy the entire base on the log directory, and execute from there.\"\n )\n parser.add_argument(\n \"--horovod\",\n default=False,\n action=\"store_true\",\n help=\"Use horovod for distributed training.\"\n )\n parser.add_argument(\n \"--ddp-static-graph\",\n default=False,\n action='store_true',\n help=\"Enable static graph optimization for DDP in PyTorch >= 1.11.\",\n )\n parser.add_argument(\n \"--no-set-device-rank\",\n default=False,\n action=\"store_true\",\n help=\"Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc).\"\n )\n parser.add_argument(\n \"--seed\", type=int, default=0, help=\"Default random seed.\"\n )\n parser.add_argument(\n \"--grad-clip-norm\", type=float, default=None, help=\"Gradient clip.\"\n )\n parser.add_argument(\n \"--lock-text\",\n default=False,\n action='store_true',\n help=\"Lock full text tower by disabling gradients.\",\n )\n parser.add_argument(\n \"--lock-text-unlocked-layers\",\n type=int,\n default=0,\n help=\"Leave last n image tower layer groups unlocked.\",\n )\n parser.add_argument(\n \"--lock-text-freeze-layer-norm\",\n default=False,\n action='store_true',\n help=\"Freeze BatchNorm running stats in image tower for any locked layers.\",\n )\n parser.add_argument(\n \"--log-every-n-steps\",\n type=int,\n default=1,\n help=\"Log every n steps to tensorboard/console/wandb.\",\n )\n parser.add_argument(\n \"--coca-caption-loss-weight\",\n type=float,\n default=2.0,\n help=\"Weight assigned to caption loss in CoCa.\"\n )\n parser.add_argument(\n \"--coca-contrastive-loss-weight\",\n type=float,\n default=1.0,\n help=\"Weight assigned to contrastive loss when training CoCa.\"\n )\n parser.add_argument(\n \"--remote-sync\",\n type=str,\n default=None,\n help=\"Optinoally sync with a remote path specified by this arg\",\n )\n parser.add_argument(\n \"--remote-sync-frequency\",\n type=int,\n default=300,\n help=\"How frequently to sync to a remote directly if --remote-sync is not None.\",\n )\n parser.add_argument(\n \"--remote-sync-protocol\",\n choices=[\"s3\", \"fsspec\"],\n default=\"s3\",\n help=\"How to do the remote sync backup if --remote-sync is not None.\",\n )\n parser.add_argument(\n \"--delete-previous-checkpoint\",\n default=False,\n action=\"store_true\",\n help=\"If true, delete previous checkpoint after storing a new one.\"\n )\n parser.add_argument(\n \"--distill-model\",\n default=None,\n help='Which model arch to distill from, if any.'\n )\n parser.add_argument(\n \"--distill-pretrained\",\n default=None,\n help='Which pre-trained weights to distill from, if any.'\n )\n\n parser.add_argument(\n \"--text_type\",\n type=str,\n default='taxon',\n help=\"Text type of annotation for text encoder.\",\n )\n args = parser.parse_args(args)\n\n # If some params are not passed, we use the default values based on model name.\n default_params = get_default_params(args.model)\n for name, val in default_params.items():\n if getattr(args, name) is None:\n setattr(args, name, val)\n\n return args\n"
},
{
"path": "src/training/precision.py",
"content": "import torch\nfrom contextlib import suppress\n\n\ndef get_autocast(precision):\n if precision == 'amp':\n return torch.cuda.amp.autocast\n elif precision == 'amp_bfloat16' or precision == 'amp_bf16':\n # amp_bfloat16 is more stable than amp float16 for clip training\n return lambda: torch.cuda.amp.autocast(dtype=torch.bfloat16)\n else:\n return suppress\n"
},
{
"path": "src/training/profile.py",
"content": "import argparse\n\nimport torch\nimport open_clip\nimport pandas as pd\nfrom fvcore.nn import FlopCountAnalysis, flop_count_str, ActivationCountAnalysis\n\n\nparser = argparse.ArgumentParser(description='OpenCLIP Profiler')\n\n# benchmark specific args\nparser.add_argument('--model', metavar='NAME', default='',\n help='model(s) to profile')\nparser.add_argument('--results-file', default='', type=str, metavar='FILENAME',\n help='Output csv file for results')\n\n\ndef profile_fvcore(\n model,\n image_input_size=(3, 224, 224),\n text_input_size=(77,),\n batch_size=1,\n detailed=False,\n force_cpu=False\n):\n if force_cpu:\n model = model.to('cpu')\n device, dtype = next(model.parameters()).device, next(model.parameters()).dtype\n example_image_input = torch.ones((batch_size,) + image_input_size, device=device, dtype=dtype)\n example_text_input = torch.ones((batch_size,) + text_input_size, device=device, dtype=torch.int64)\n fca = FlopCountAnalysis(model, (example_image_input, example_text_input))\n aca = ActivationCountAnalysis(model, (example_image_input, example_text_input))\n if detailed:\n fcs = flop_count_str(fca)\n print(fcs)\n return fca.total(), aca.total()\n\n\ndef profile_fvcore_text(\n model,\n text_input_size=(77,),\n batch_size=1,\n detailed=False,\n force_cpu=False\n):\n if force_cpu:\n model = model.to('cpu')\n device = next(model.parameters()).device\n example_input = torch.ones((batch_size,) + text_input_size, device=device, dtype=torch.int64)\n fca = FlopCountAnalysis(model, example_input)\n aca = ActivationCountAnalysis(model, example_input)\n if detailed:\n fcs = flop_count_str(fca)\n print(fcs)\n return fca.total(), aca.total()\n\n\ndef profile_fvcore_image(\n model,\n image_input_size=(3, 224, 224),\n batch_size=1,\n detailed=False,\n force_cpu=False\n):\n if force_cpu:\n model = model.to('cpu')\n device, dtype = next(model.parameters()).device, next(model.parameters()).dtype\n example_input = torch.ones((batch_size,) + image_input_size, device=device, dtype=dtype)\n fca = FlopCountAnalysis(model, example_input)\n aca = ActivationCountAnalysis(model, example_input)\n if detailed:\n fcs = flop_count_str(fca)\n print(fcs)\n return fca.total(), aca.total()\n\n\ndef count_params(model):\n return sum([m.numel() for m in model.parameters()])\n\n\ndef profile_model(model_name):\n model = open_clip.create_model(model_name, force_custom_text=True, pretrained_hf=False)\n model.eval()\n if torch.cuda.is_available():\n model = model.cuda()\n\n if isinstance(model.visual.image_size, (tuple, list)):\n image_input_size = (3,) + tuple(model.visual.image_size[-2:])\n else:\n image_input_size = (3, model.visual.image_size, model.visual.image_size)\n text_input_size = (77,)\n\n results = {}\n results['model'] = model_name\n results['image_size'] = image_input_size[1]\n\n model_cfg = open_clip.get_model_config(model_name)\n if model_cfg:\n vision_cfg = open_clip.CLIPVisionCfg(**model_cfg['vision_cfg'])\n text_cfg = open_clip.CLIPTextCfg(**model_cfg['text_cfg'])\n results['image_width'] = int(vision_cfg.width)\n results['text_width'] = int(text_cfg.width)\n results['embed_dim'] = int(model_cfg['embed_dim'])\n else:\n results['image_width'] = 0\n results['text_width'] = 0\n results['embed_dim'] = 0\n\n retries = 2\n while retries:\n retries -= 1\n try:\n macs, acts = profile_fvcore(\n model, image_input_size=image_input_size, text_input_size=text_input_size, force_cpu=not retries)\n\n image_macs, image_acts = profile_fvcore_image(\n model.visual, image_input_size=image_input_size, force_cpu=not retries)\n\n text_macs, text_acts = profile_fvcore_text(\n model.text, text_input_size=text_input_size, force_cpu=not retries)\n\n results['gmacs'] = round(macs / 1e9, 2)\n results['macts'] = round(acts / 1e6, 2)\n results['mparams'] = round(count_params(model) / 1e6, 2)\n results['image_gmacs'] = round(image_macs / 1e9, 2)\n results['image_macts'] = round(image_acts / 1e6, 2)\n results['image_mparams'] = round(count_params(model.visual) / 1e6, 2)\n results['text_gmacs'] = round(text_macs / 1e9, 2)\n results['text_macts'] = round(text_acts / 1e6, 2)\n results['text_mparams'] = round(count_params(model.text) / 1e6, 2)\n except RuntimeError as e:\n pass\n return results\n\n\ndef main():\n args = parser.parse_args()\n\n # FIXME accept a text file name to allow lists of models in txt/csv\n if args.model == 'all':\n parsed_model = open_clip.list_models()\n else:\n parsed_model = args.model.split(',')\n\n results = []\n for m in parsed_model:\n row = profile_model(m)\n results.append(row)\n\n df = pd.DataFrame(results, columns=results[0].keys())\n df = df.sort_values('gmacs')\n print(df)\n if args.results_file:\n df.to_csv(args.results_file, index=False)\n\n\nif __name__ == '__main__':\n main()\n"
},
{
"path": "src/training/scheduler.py",
"content": "import numpy as np\n\n\ndef assign_learning_rate(optimizer, new_lr):\n for param_group in optimizer.param_groups:\n param_group[\"lr\"] = new_lr\n\n\ndef _warmup_lr(base_lr, warmup_length, step):\n return base_lr * (step + 1) / warmup_length\n\n\ndef const_lr(optimizer, base_lr, warmup_length, steps):\n def _lr_adjuster(step):\n if step < warmup_length:\n lr = _warmup_lr(base_lr, warmup_length, step)\n else:\n lr = base_lr\n assign_learning_rate(optimizer, lr)\n return lr\n return _lr_adjuster\n\n\ndef const_lr_cooldown(optimizer, base_lr, warmup_length, steps, cooldown_steps, cooldown_power=1.0, cooldown_end_lr=0.):\n def _lr_adjuster(step):\n start_cooldown_step = steps - cooldown_steps\n if step < warmup_length:\n lr = _warmup_lr(base_lr, warmup_length, step)\n else:\n if step < start_cooldown_step:\n lr = base_lr\n else:\n e = step - start_cooldown_step\n es = steps - start_cooldown_step\n # linear decay if power == 1; polynomial decay otherwise;\n decay = (1 - (e/es)) ** cooldown_power\n lr = decay * (base_lr - cooldown_end_lr) + cooldown_end_lr\n assign_learning_rate(optimizer, lr)\n return lr\n return _lr_adjuster\n\n\ndef cosine_lr(optimizer, base_lr, warmup_length, steps):\n def _lr_adjuster(step):\n if step < warmup_length:\n lr = _warmup_lr(base_lr, warmup_length, step)\n else:\n e = step - warmup_length\n es = steps - warmup_length\n lr = 0.5 * (1 + np.cos(np.pi * e / es)) * base_lr\n assign_learning_rate(optimizer, lr)\n return lr\n return _lr_adjuster\n"
},
{
"path": "src/training/train.py",
"content": "import json\nimport logging\nimport math\nimport os\nimport random\nimport time\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.parallel.distributed import DistributedDataParallel\n\ntry:\n import wandb\nexcept ImportError:\n wandb = None\n\nfrom ..open_clip import get_cast_dtype, CLIP, CustomTextCLIP\nfrom .distributed import is_master\nfrom .zero_shot import zero_shot_eval\nfrom .precision import get_autocast\n\n\nclass AverageMeter(object):\n \"\"\"Computes and stores the average and current value\"\"\"\n\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\ndef postprocess_clip_output(model_out):\n return {\n \"image_features\": model_out[0],\n \"text_features\": model_out[1],\n \"logit_scale\": model_out[2]\n }\n\ndef unwrap_model(model):\n if hasattr(model, 'module'):\n return model.module\n else:\n return model\n\n\ndef backward(total_loss, scaler):\n if scaler is not None:\n scaler.scale(total_loss).backward()\n else:\n total_loss.backward()\n\n\ndef train_one_epoch(model, data, loss, epoch, optimizer, scaler, scheduler, dist_model, args, tb_writer=None):\n device = torch.device(args.device)\n autocast = get_autocast(args.precision)\n cast_dtype = get_cast_dtype(args.precision)\n\n\n model.train()\n if args.distill:\n dist_model.eval()\n\n data['train'].set_epoch(epoch) # set epoch in process safe manner via sampler or shared_epoch\n dataloader = data['train'].dataloader\n num_batches_per_epoch = dataloader.num_batches // args.accum_freq\n sample_digits = math.ceil(math.log(dataloader.num_samples + 1, 10))\n\n if args.accum_freq > 1:\n accum_images, accum_texts, accum_features = [], [], {}\n\n losses_m = {}\n batch_time_m = AverageMeter()\n data_time_m = AverageMeter()\n end = time.time()\n for i, batch in enumerate(dataloader):\n i_accum = i // args.accum_freq\n step = num_batches_per_epoch * epoch + i_accum\n\n if not args.skip_scheduler:\n scheduler(step)\n\n # Randomly choose a text type for this batch\n if args.text_type == 'random':\n images, sci, com, taxon, sci_com, taxon_com = batch\n random.seed(step)\n texts = random.choice([sci, com, taxon, sci_com, taxon_com])\n else:\n images, texts = batch\n images = images.to(device=device, dtype=cast_dtype, non_blocking=True)\n texts = texts.to(device=device, non_blocking=True)\n\n data_time_m.update(time.time() - end)\n optimizer.zero_grad()\n\n if args.accum_freq == 1:\n with autocast():\n model_out = model(images, texts)\n logit_scale = model_out[\"logit_scale\"]\n if args.distill:\n with torch.no_grad():\n dist_model_out = dist_model(images, texts)\n model_out.update({f'dist_{k}' : v for k, v in dist_model_out.items()})\n losses = loss(**model_out, output_dict=True)\n\n total_loss = sum(losses.values())\n losses[\"loss\"] = total_loss\n\n backward(total_loss, scaler)\n else:\n # First, cache the features without any gradient tracking.\n with torch.no_grad():\n with autocast():\n model_out = model(images, texts)\n model_out.pop(\"logit_scale\")\n for key, val in model_out.items():\n if key in accum_features:\n accum_features[key].append(val)\n else:\n accum_features[key] = [val]\n\n accum_images.append(images)\n accum_texts.append(texts)\n\n # If (i + 1) % accum_freq is not zero, move on to the next batch.\n if ((i + 1) % args.accum_freq) > 0:\n # FIXME this makes data time logging unreliable when accumulating\n continue\n\n # Now, ready to take gradients for the last accum_freq batches.\n # Re-do the forward pass for those batches, and use the cached features from the other batches as negatives.\n # Call backwards each time, but only step optimizer at the end.\n optimizer.zero_grad()\n for j in range(args.accum_freq):\n images = accum_images[j]\n texts = accum_texts[j]\n with autocast():\n model_out = model(images, texts)\n logit_scale = model_out.pop(\"logit_scale\")\n inputs = {}\n for key, val in accum_features.items():\n accumulated = accum_features[key]\n inputs[key] = torch.cat(accumulated[:j] + [model_out[key]] + accumulated[j + 1:])\n losses = loss(**inputs, logit_scale=logit_scale, output_dict=True)\n del inputs\n total_loss = sum(losses.values())\n losses[\"loss\"] = total_loss\n backward(total_loss, scaler)\n\n if scaler is not None:\n if args.horovod:\n optimizer.synchronize()\n scaler.unscale_(optimizer)\n if args.grad_clip_norm is not None:\n torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)\n with optimizer.skip_synchronize():\n scaler.step(optimizer)\n else:\n if args.grad_clip_norm is not None:\n scaler.unscale_(optimizer)\n torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)\n scaler.step(optimizer)\n scaler.update()\n else:\n if args.grad_clip_norm is not None:\n torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)\n optimizer.step()\n\n # reset gradient accum, if enabled\n if args.accum_freq > 1:\n accum_images, accum_texts, accum_features = [], [], {}\n\n # Note: we clamp to 4.6052 = ln(100), as in the original paper.\n with torch.no_grad():\n unwrap_model(model).logit_scale.clamp_(0, math.log(100))\n\n batch_time_m.update(time.time() - end)\n end = time.time()\n batch_count = i_accum + 1\n if is_master(args) and (i_accum % args.log_every_n_steps == 0 or batch_count == num_batches_per_epoch):\n batch_size = len(images)\n num_samples = batch_count * batch_size * args.accum_freq * args.world_size\n samples_per_epoch = dataloader.num_samples\n percent_complete = 100.0 * batch_count / num_batches_per_epoch\n\n # NOTE loss is coarsely sampled, just master node and per log update\n for key, val in losses.items():\n if key not in losses_m:\n losses_m[key] = AverageMeter()\n losses_m[key].update(val.item(), batch_size)\n\n logit_scale_scalar = logit_scale.item()\n loss_log = \" \".join(\n [\n f\"{loss_name.capitalize()}: {loss_m.val:#.5g} ({loss_m.avg:#.5g})\" \n for loss_name, loss_m in losses_m.items()\n ]\n )\n samples_per_second = args.accum_freq * args.batch_size * args.world_size / batch_time_m.val\n samples_per_second_per_gpu = args.accum_freq * args.batch_size / batch_time_m.val\n logging.info(\n f\"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] \"\n f\"Data (t): {data_time_m.avg:.3f} \"\n f\"Batch (t): {batch_time_m.avg:.3f}, {samples_per_second:#g}/s, {samples_per_second_per_gpu:#g}/s/gpu \"\n f\"LR: {optimizer.param_groups[0]['lr']:5f} \"\n f\"Logit Scale: {logit_scale_scalar:.3f} \" + loss_log\n )\n\n # Save train loss / etc. Using non avg meter values as loggers have their own smoothing\n log_data = {\n \"data_time\": data_time_m.val,\n \"batch_time\": batch_time_m.val,\n \"samples_per_second\": samples_per_second,\n \"samples_per_second_per_gpu\": samples_per_second_per_gpu,\n \"scale\": logit_scale_scalar,\n \"lr\": optimizer.param_groups[0][\"lr\"]\n } \n log_data.update({name:val.val for name,val in losses_m.items()})\n\n for name, val in log_data.items():\n name = \"train/\" + name\n if tb_writer is not None:\n tb_writer.add_scalar(name, val, step)\n if args.wandb:\n assert wandb is not None, 'Please install wandb.'\n wandb.log({name: val, 'step': step})\n\n # resetting batch / data time meters per log window\n batch_time_m.reset()\n data_time_m.reset()\n # end for\n\n\ndef evaluate(model, data, epoch, args, tb_writer=None):\n metrics = {}\n if not is_master(args):\n return\n device = torch.device(args.device)\n model.eval()\n\n zero_shot_metrics = zero_shot_eval(model, data, epoch, args)\n metrics.update(zero_shot_metrics)\n\n autocast = get_autocast(args.precision)\n cast_dtype = get_cast_dtype(args.precision)\n\n if 'val' in data and (args.val_frequency and ((epoch % args.val_frequency) == 0 or epoch == args.epochs)):\n dataloader = data['val'].dataloader\n num_samples = 0\n samples_per_val = dataloader.num_samples\n\n cumulative_loss = 0.0\n cumulative_gen_loss = 0.0\n with torch.no_grad():\n for i, batch in enumerate(dataloader):\n if args.text_type == 'random':\n images, sci, com, taxon, sci_com, taxon_com = batch\n random.seed(i)\n texts = random.choice([sci, com, taxon, sci_com, taxon_com])\n else:\n images, texts = batch\n images = images.to(device=device, dtype=cast_dtype, non_blocking=True)\n texts = texts.to(device=device, non_blocking=True)\n\n with autocast():\n model_out = model(images, texts)\n image_features = model_out[\"image_features\"]\n text_features = model_out[\"text_features\"]\n logit_scale = model_out[\"logit_scale\"]\n logit_scale = logit_scale.mean()\n logits_per_image = logit_scale * image_features @ text_features.t()\n logits_per_text = logits_per_image.t()\n\n batch_size = images.shape[0]\n labels = torch.arange(batch_size, device=device).long()\n total_loss = (\n F.cross_entropy(logits_per_image, labels) +\n F.cross_entropy(logits_per_text, labels)\n ) / 2\n\n gen_loss = maybe_compute_generative_loss(model_out)\n\n cumulative_loss += total_loss * batch_size\n num_samples += batch_size\n if is_master(args) and (i % 100) == 0:\n logging.info(\n f\"Eval Epoch: {epoch} [{num_samples} / {samples_per_val}]\\t\"\n f\"Clip Loss: {cumulative_loss / num_samples:.6f}\\t\")\n\n if gen_loss is not None:\n cumulative_gen_loss += gen_loss * batch_size\n logging.info(\n f\"Generative Loss: {cumulative_gen_loss / num_samples:.6f}\\t\")\n\n\n loss = cumulative_loss / num_samples\n metrics.update({\n \"clip_val_loss\": loss.item(), \n \"epoch\": epoch, \n \"num_samples\": num_samples\n })\n if gen_loss is not None:\n gen_loss = cumulative_gen_loss / num_samples\n metrics.update({\"val_generative_loss\": gen_loss.item()})\n\n if not metrics:\n return\n\n logging.info(\n f\"Eval Epoch: {epoch} \"\n + \"\\t\".join([f\"{k}: {round(v, 4):.4f}\" for k, v in metrics.items()])\n )\n\n if args.save_logs:\n if tb_writer is not None:\n for name, val in metrics.items():\n tb_writer.add_scalar(f\"val/{name}\", val, epoch)\n\n with open(os.path.join(args.checkpoint_path, \"results.jsonl\"), \"a+\") as f:\n f.write(json.dumps(metrics))\n f.write(\"\\n\")\n\n if args.report_to == 'wandb':\n assert wandb is not None, 'Please install wandb.'\n for name, val in metrics.items():\n wandb.log({f\"val/{name}\": val, 'epoch': epoch})\n\n\ndef get_clip_metrics(image_features, text_features, logit_scale, device):\n metrics = {}\n logits_per_image = (logit_scale * image_features.to(device) @ text_features.to(device).t()).cpu()\n logits_per_text = logits_per_image.t()\n logits = {\"image_to_text\": logits_per_image, \"text_to_image\": logits_per_text}\n\n ground_truth = torch.arange(len(text_features)).view(-1, 1)\n\n for name, logit in logits.items():\n # argsort is also quite slow (20-30 seconds)\n ranking = torch.argsort(logit, descending=True, dim=1)\n preds = torch.where(ranking == ground_truth)[1]\n preds = preds.numpy()\n metrics[f\"{name}_mean_rank\"] = preds.mean() + 1\n metrics[f\"{name}_median_rank\"] = np.floor(np.median(preds)) + 1\n for k in [1, 5, 10]:\n metrics[f\"{name}_R@{k}\"] = np.mean(preds < k)\n\n return metrics\n\n\ndef maybe_compute_generative_loss(model_out):\n if \"logits\" in model_out and \"labels\" in model_out:\n token_logits = model_out[\"logits\"]\n token_labels = model_out[\"labels\"]\n return F.cross_entropy(token_logits.permute(0, 2, 1), token_labels)\n"
},
{
"path": "src/training/zero_shot.py",
"content": "import logging\n\nimport torch\nimport torch.nn.functional as F\nfrom tqdm import tqdm\n\nfrom ..open_clip import get_cast_dtype, get_tokenizer\nfrom .precision import get_autocast\nfrom .imagenet_zeroshot_data import imagenet_classnames, openai_imagenet_template\n\n\ndef zero_shot_classifier(model, classnames, templates, args):\n tokenizer = get_tokenizer(args.model)\n with torch.no_grad():\n zeroshot_weights = []\n for classname in tqdm(classnames):\n texts = [template(classname) for template in templates] # format with class\n texts = tokenizer(texts).to(args.device) # tokenize\n if args.distributed and not args.horovod:\n class_embeddings = model.module.encode_text(texts)\n else:\n class_embeddings = model.encode_text(texts)\n class_embedding = F.normalize(class_embeddings, dim=-1).mean(dim=0)\n class_embedding /= class_embedding.norm()\n zeroshot_weights.append(class_embedding)\n zeroshot_weights = torch.stack(zeroshot_weights, dim=1).to(args.device)\n return zeroshot_weights\n\n\ndef 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 [float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy()) for k in topk]\n\n\ndef run(model, classifier, dataloader, args):\n autocast = get_autocast(args.precision)\n cast_dtype = get_cast_dtype(args.precision)\n with torch.no_grad():\n top1, top5, n = 0., 0., 0.\n for images, target in tqdm(dataloader, unit_scale=args.batch_size):\n images = images.to(args.device)\n if cast_dtype is not None:\n images = images.to(dtype=cast_dtype)\n target = target.to(args.device)\n\n with autocast():\n # predict\n if args.distributed and not args.horovod:\n image_features = model.module.encode_image(images)\n else:\n image_features = model.encode_image(images)\n image_features = F.normalize(image_features, dim=-1)\n logits = 100. * image_features @ classifier\n\n # measure accuracy\n acc1, acc5 = accuracy(logits, target, topk=(1, 5))\n top1 += acc1\n top5 += acc5\n n += images.size(0)\n\n top1 = (top1 / n)\n top5 = (top5 / n)\n return top1, top5\n\n\ndef inat21_class_preprocess(cls):\n num, *tiers = cls.split('_')\n return ' '.join(tiers)\n\n\ndef zero_shot_eval(model, data, epoch, args):\n if 'imagenet-val' not in data and 'imagenet-v2' not in data:\n return {}\n if args.zeroshot_frequency == 0:\n return {}\n if (epoch % args.zeroshot_frequency) != 0 and epoch != args.epochs:\n return {}\n\n logging.info('Starting zero-shot imagenet.')\n\n logging.info('Building zero-shot classifier')\n # To do zero-shot eval on iNat21 classes, we use the class names directly.\n # They are stored in the dataset in the .classes attribute\n logging.warn(\"Using iNat21 classes instead of imagenet!\")\n inat_classnames = [\n inat21_class_preprocess(c)\n for c in data['imagenet-val'].dataloader.dataset.classes\n ]\n classifier = zero_shot_classifier(model, inat_classnames, openai_imagenet_template, args)\n\n logging.info('Using classifier')\n results = {}\n if 'imagenet-val' in data:\n top1, top5 = run(model, classifier, data['imagenet-val'].dataloader, args)\n results['imagenet-zeroshot-val-top1'] = top1\n results['imagenet-zeroshot-val-top5'] = top5\n if 'imagenet-v2' in data:\n top1, top5 = run(model, classifier, data['imagenet-v2'].dataloader, args)\n results['imagenetv2-zeroshot-val-top1'] = top1\n results['imagenetv2-zeroshot-val-top5'] = top5\n\n logging.info('Finished zero-shot imagenet.')\n\n return results\n"
},
{
"path": "train.sh",
"content": "#!/bin/bash\n\nexport CUDA_VISIBLE_DEVICES=0,1,2,3\n\ntorchrun --nproc_per_node 4 -m src.training.main \\\n --train-data './data/train/shard-{000000..000159}.tar' \\\n --val-data './data/val/shard-{000000..000031}.tar' \\\n --dataset-type 'webdataset' \\\n --pretrained 'openai' \\\n --text_type 'random' \\\n --warmup 1000 \\\n --batch-size 4096 \\\n --accum-freq 1 \\\n --epochs 100 \\\n --workers 8 \\\n --model ViT-B-16 \\\n --lr 1e-4 \\\n --log-every-n-steps 1 \\\n --dataset-resampled \\\n --local-loss \\\n --gather-with-grad \\\n --grad-checkpointing \\\n --logs '../storage/log/' \\\n"
}
]