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class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 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14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 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1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 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<path d="M10.3 6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2"> Search syntax tips </a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="feedback-dialog-title"> <div data-view-component="true" class="Overlay-body"> </option></form><form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="pqXlDlVfToYJXj19wp/FAwngwfMIv8sAt3oL33g/val79OktSq6EZLG0vI2uwqXvwq7wP5gN1UySJ8Ynri6i9w==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium 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data-canonical-src=\"https://img.shields.io/badge/python-3.7%7C3.8%7C3.9%2B-blueviolet\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\t\n\t\u003ca href=\"https://pythae.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\n \t\u003cimg src=\"https://camo.githubusercontent.com/242ecca3a690bb011d380a2b9f8b1039fd862150aa7c7881459bf3719ceb2694/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f7079746861652f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/pythae/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\n\t\u003c/a\u003e\n\t\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" rel=\"nofollow\"\u003e\n\t \u003cimg src=\"https://camo.githubusercontent.com/de06fa14249bd07153ac5fd470b1d0b1d3301907959f1d48d424bbbed4440282/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f636c656d656e7463686164656265632f62656e63686d61726b5f5641453f636f6c6f723d626c7565\" data-canonical-src=\"https://img.shields.io/github/license/clementchadebec/benchmark_VAE?color=blue\" style=\"max-width: 100%;\"\u003e\n\t\u003c/a\u003e\u003cbr\u003e\n \n\t \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/a41c88e230f45ad28a302c9a5e09c79901e172d8d81499fcffc16c191819b5a9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d626c61636b\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a41c88e230f45ad28a302c9a5e09c79901e172d8d81499fcffc16c191819b5a9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d626c61636b\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-black\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\t\n\t\u003ca href=\"https://codecov.io/gh/clementchadebec/benchmark_VAE\" rel=\"nofollow\"\u003e\n \t\t\u003cimg src=\"https://camo.githubusercontent.com/c6ea254f39829d7799c24b0e3ba743846386f5a59dfcc04e8d629ca111f20497/68747470733a2f2f636f6465636f762e696f2f67682f636c656d656e7463686164656265632f62656e63686d61726b5f5641452f6272616e63682f6d61696e2f67726170682f62616467652e7376673f746f6b656e3d4b454d374b4b4953584a\" data-canonical-src=\"https://codecov.io/gh/clementchadebec/benchmark_VAE/branch/main/graph/badge.svg?token=KEM7KKISXJ\" style=\"max-width: 100%;\"\u003e\n\t\u003c/a\u003e\n\t\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/overview_notebook.ipynb\" rel=\"nofollow\"\u003e\n \t\t\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n\t\u003c/a\u003e\n\t\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca href=\"https://pythae.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003epythae\u003c/h1\u003e\u003ca id=\"user-content-pythae\" class=\"anchor\" aria-label=\"Permalink: pythae\" href=\"#pythae\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis library implements some of the most common (Variational) Autoencoder models under a unified implementation. In particular, it\nprovides the possibility to perform benchmark experiments and comparisons by training\nthe models with the same autoencoding neural network architecture. The feature \u003cem\u003emake your own autoencoder\u003c/em\u003e\nallows you to train any of these models with your own data and own Encoder and Decoder neural networks. It integrates experiment monitoring tools such \u003ca href=\"https://wandb.ai/\" rel=\"nofollow\"\u003ewandb\u003c/a\u003e, \u003ca href=\"https://mlflow.org/\" rel=\"nofollow\"\u003emlflow\u003c/a\u003e or \u003ca href=\"https://www.comet.com/signup?utm_source=pythae\u0026amp;utm_medium=partner\u0026amp;utm_campaign=AMS_US_EN_SNUP_Pythae_Comet_Integration\" rel=\"nofollow\"\u003ecomet-ml\u003c/a\u003e 🧪 and allows model sharing and loading from the \u003ca href=\"https://huggingface.co/models\" rel=\"nofollow\"\u003eHuggingFace Hub\u003c/a\u003e 🤗 in a few lines of code.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNews\u003c/strong\u003e 📢\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAs of v0.1.0, \u003ccode\u003ePythae\u003c/code\u003e now supports distributed training using PyTorch's \u003ca href=\"https://pytorch.org/docs/stable/notes/ddp.html\" rel=\"nofollow\"\u003eDDP\u003c/a\u003e. You can now train your favorite VAE faster and on larger datasets, still with a few lines of code.\nSee our speed-up \u003ca href=\"#benchmark\"\u003ebenchmark\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick access:\u003c/h2\u003e\u003ca id=\"user-content-quick-access\" class=\"anchor\" aria-label=\"Permalink: Quick access:\" href=\"#quick-access\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#available-models\"\u003eImplemented models\u003c/a\u003e / \u003ca href=\"#available-samplers\"\u003eImplemented samplers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#reproducibility\"\u003eReproducibility statement\u003c/a\u003e / \u003ca href=\"#results\"\u003eResults flavor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#launching-a-model-training\"\u003eModel training\u003c/a\u003e / \u003ca href=\"#launching-data-generation\"\u003eData generation\u003c/a\u003e / \u003ca href=\"#define-you-own-autoencoder-architecture\"\u003eCustom network architectures\u003c/a\u003e / \u003ca href=\"#distributed-training-with-pythae\"\u003eDistributed training\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#sharing-your-models-with-the-huggingface-hub-\"\u003eModel sharing with 🤗 Hub\u003c/a\u003e / \u003ca href=\"#monitoring-your-experiments-with-wandb-\"\u003eExperiment tracking with \u003ccode\u003ewandb\u003c/code\u003e\u003c/a\u003e / \u003ca href=\"#monitoring-your-experiments-with-mlflow-\"\u003eExperiment tracking with \u003ccode\u003emlflow\u003c/code\u003e\u003c/a\u003e / \u003ca href=\"#monitoring-your-experiments-with-comet_ml-\"\u003eExperiment tracking with \u003ccode\u003ecomet_ml\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#getting-your-hands-on-the-code\"\u003eTutorials\u003c/a\u003e / \u003ca href=\"https://pythae.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#contributing-\"\u003eContributing 🚀\u003c/a\u003e / \u003ca href=\"#dealing-with-issues-%EF%B8%8F\"\u003eIssues 🛠️\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#citation\"\u003eCiting this repository\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install the latest stable release of this library run the following using \u003ccode\u003epip\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip install pythae\"\u003e\u003cpre\u003e$ pip install pythae\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install the latest github version of this library run the following using \u003ccode\u003epip\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip install git+https://github.com/clementchadebec/benchmark_VAE.git\"\u003e\u003cpre\u003e$ pip install git+https://github.com/clementchadebec/benchmark_VAE.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eor alternatively you can clone the github repo to access to tests, tutorials and scripts.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ git clone https://github.com/clementchadebec/benchmark_VAE.git\"\u003e\u003cpre\u003e$ git clone https://github.com/clementchadebec/benchmark_VAE.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eand install the library\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ cd benchmark_VAE\n$ pip install -e .\"\u003e\u003cpre\u003e$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e benchmark_VAE\n$ pip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAvailable Models\u003c/h2\u003e\u003ca id=\"user-content-available-models\" class=\"anchor\" aria-label=\"Permalink: Available Models\" href=\"#available-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBelow is the list of the models currently implemented in the library.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eModels\u003c/th\u003e\n\u003cth align=\"center\"\u003eTraining example\u003c/th\u003e\n\u003cth align=\"center\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003eOfficial Implementation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eAutoencoder (AE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/ae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVariational Autoencoder (VAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1312.6114\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBeta Variational Autoencoder (BetaVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/beta_vae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://openreview.net/pdf?id=Sy2fzU9gl\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE with Linear Normalizing Flows (VAE_LinNF)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_lin_nf_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1505.05770\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE with Inverse Autoregressive Flows (VAE_IAF)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_iaf_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1606.04934\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/openai/iaf\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eDisentangled Beta Variational Autoencoder (DisentangledBetaVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/disentangled_beta_vae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1804.03599\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eDisentangling by Factorising (FactorVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/factor_vae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1802.05983\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBeta-TC-VAE (BetaTCVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/beta_tc_vae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1802.04942\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/rtqichen/beta-tcvae\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eImportance Weighted Autoencoder (IWAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/iwae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1509.00519v4\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/yburda/iwae\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eMultiply Importance Weighted Autoencoder (MIWAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/miwae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1802.04537\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ePartially Importance Weighted Autoencoder (PIWAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/piwae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1802.04537\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eCombination Importance Weighted Autoencoder (CIWAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/ciwae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1802.04537\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE with perceptual metric similarity (MSSSIM_VAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/ms_ssim_vae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1511.06409\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eWasserstein Autoencoder (WAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/wae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1711.01558\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/tolstikhin/wae\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eInfo Variational Autoencoder (INFOVAE_MMD)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/info_vae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1706.02262\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAMP Autoencoder (VAMP)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vamp_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1705.07120\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/jmtomczak/vae_vampprior\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHyperspherical VAE (SVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/svae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1804.00891\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/nicola-decao/s-vae-pytorch\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ePoincaré Disk VAE (PoincareVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/pvae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1901.06033\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/emilemathieu/pvae\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eAdversarial Autoencoder (Adversarial_AE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/adversarial_ae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1511.05644\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVariational Autoencoder GAN (VAEGAN) 🥗\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vaegan_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1512.09300\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/andersbll/autoencoding_beyond_pixels\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVector Quantized VAE (VQVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vqvae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1711.00937\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/deepmind/sonnet/blob/v2/sonnet/\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHamiltonian VAE (HVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/hvae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1805.11328\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/anthonycaterini/hvae-nips\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRegularized AE with L2 decoder param (RAE_L2)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rae_l2_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1903.12436\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ParthaEth/Regularized_autoencoders-RAE-/tree/master/\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRegularized AE with gradient penalty (RAE_GP)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rae_gp_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1903.12436\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ParthaEth/Regularized_autoencoders-RAE-/tree/master/\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRiemannian Hamiltonian VAE (RHVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rhvae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/2105.00026\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/clementchadebec/pyraug\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHierarchical Residual Quantization (HRQVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/hrqvae_training.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://aclanthology.org/2022.acl-long.178/\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/tomhosking/hrq-vae\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSee \u003ca href=\"#Reconstruction\"\u003ereconstruction\u003c/a\u003e and \u003ca href=\"#Generation\"\u003egeneration\u003c/a\u003e results for all aforementionned models\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAvailable Samplers\u003c/h2\u003e\u003ca id=\"user-content-available-samplers\" class=\"anchor\" aria-label=\"Permalink: Available Samplers\" href=\"#available-samplers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBelow is the list of the models currently implemented in the library.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eSamplers\u003c/th\u003e\n\u003cth align=\"center\"\u003eModels\u003c/th\u003e\n\u003cth align=\"center\"\u003ePaper\u003c/th\u003e\n\u003cth align=\"center\"\u003eOfficial Implementation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eNormal prior (NormalSampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eall models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1312.6114\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eGaussian mixture (GaussianMixtureSampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eall models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1903.12436\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/ParthaEth/Regularized_autoencoders-RAE-/tree/master/models/rae\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eTwo stage VAE sampler (TwoStageVAESampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eall VAE based models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://openreview.net/pdf?id=B1e0X3C9tQ\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/daib13/TwoStageVAE/\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eUnit sphere uniform sampler (HypersphereUniformSampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eSVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1804.00891\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/nicola-decao/s-vae-pytorch\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ePoincaré Disk sampler (PoincareDiskSampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003ePoincareVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1901.06033\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/emilemathieu/pvae\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAMP prior sampler (VAMPSampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVAMP\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1705.07120\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/jmtomczak/vae_vampprior\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eManifold sampler (RHVAESampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eRHVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/2105.00026\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/clementchadebec/pyraug\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eMasked Autoregressive Flow Sampler (MAFSampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eall models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1705.07057v4\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/gpapamak/maf\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eInverse Autoregressive Flow Sampler (IAFSampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eall models\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1606.04934\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://github.com/openai/iaf\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003ePixelCNN (PixelCNNSampler)\u003c/td\u003e\n\u003ctd align=\"center\"\u003eVQVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://arxiv.org/abs/1606.05328\" rel=\"nofollow\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReproducibility\u003c/h2\u003e\u003ca id=\"user-content-reproducibility\" class=\"anchor\" aria-label=\"Permalink: Reproducibility\" href=\"#reproducibility\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe validate the implementations by reproducing some results presented in the original publications when the official code has been released or when enough details about the experimental section of the papers were available. See \u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts/reproducibility\"\u003ereproducibility\u003c/a\u003e for more details.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLaunching a model training\u003c/h2\u003e\u003ca id=\"user-content-launching-a-model-training\" class=\"anchor\" aria-label=\"Permalink: Launching a model training\" href=\"#launching-a-model-training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo launch a model training, you only need to call a \u003ccode\u003eTrainingPipeline\u003c/code\u003e instance.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from pythae.pipelines import TrainingPipeline\n\u0026gt;\u0026gt;\u0026gt; from pythae.models import VAE, VAEConfig\n\u0026gt;\u0026gt;\u0026gt; from pythae.trainers import BaseTrainerConfig\n\n\u0026gt;\u0026gt;\u0026gt; # Set up the training configuration\n\u0026gt;\u0026gt;\u0026gt; my_training_config = BaseTrainerConfig(\n...\toutput_dir='my_model',\n...\tnum_epochs=50,\n...\tlearning_rate=1e-3,\n...\tper_device_train_batch_size=200,\n...\tper_device_eval_batch_size=200,\n...\ttrain_dataloader_num_workers=2,\n...\teval_dataloader_num_workers=2,\n...\tsteps_saving=20,\n...\toptimizer_cls=\u0026quot;AdamW\u0026quot;,\n...\toptimizer_params={\u0026quot;weight_decay\u0026quot;: 0.05, \u0026quot;betas\u0026quot;: (0.91, 0.995)},\n...\tscheduler_cls=\u0026quot;ReduceLROnPlateau\u0026quot;,\n...\tscheduler_params={\u0026quot;patience\u0026quot;: 5, \u0026quot;factor\u0026quot;: 0.5}\n... )\n\u0026gt;\u0026gt;\u0026gt; # Set up the model configuration \n\u0026gt;\u0026gt;\u0026gt; my_vae_config = model_config = VAEConfig(\n...\tinput_dim=(1, 28, 28),\n...\tlatent_dim=10\n... )\n\u0026gt;\u0026gt;\u0026gt; # Build the model\n\u0026gt;\u0026gt;\u0026gt; my_vae_model = VAE(\n...\tmodel_config=my_vae_config\n... )\n\u0026gt;\u0026gt;\u0026gt; # Build the Pipeline\n\u0026gt;\u0026gt;\u0026gt; pipeline = TrainingPipeline(\n... \ttraining_config=my_training_config,\n... \tmodel=my_vae_model\n... )\n\u0026gt;\u0026gt;\u0026gt; # Launch the Pipeline\n\u0026gt;\u0026gt;\u0026gt; pipeline(\n...\ttrain_data=your_train_data, # must be torch.Tensor, np.array or torch datasets\n...\teval_data=your_eval_data # must be torch.Tensor, np.array or torch datasets\n... )\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epipelines\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTrainingPipeline\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eVAE\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eVAEConfig\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etrainers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBaseTrainerConfig\u003c/span\u003e\n\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Set up the training configuration\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_training_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBaseTrainerConfig\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003eoutput_dir\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'my_model'\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003enum_epochs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003elearning_rate\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1e-3\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eper_device_train_batch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e200\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eper_device_eval_batch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e200\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003etrain_dataloader_num_workers\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eeval_dataloader_num_workers\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003esteps_saving\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eoptimizer_cls\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"AdamW\"\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eoptimizer_params\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e{\u003cspan class=\"pl-s\"\u003e\"weight_decay\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.05\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"betas\"\u003c/span\u003e: (\u003cspan class=\"pl-c1\"\u003e0.91\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0.995\u003c/span\u003e)},\n...\t\u003cspan class=\"pl-s1\"\u003escheduler_cls\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"ReduceLROnPlateau\"\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003escheduler_params\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e{\u003cspan class=\"pl-s\"\u003e\"patience\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"factor\"\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0.5\u003c/span\u003e}\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Set up the model configuration \u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_vae_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eVAEConfig\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003einput_dim\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e28\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e28\u003c/span\u003e),\n...\t\u003cspan class=\"pl-s1\"\u003elatent_dim\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Build the model\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_vae_model\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eVAE\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003emodel_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_vae_config\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Build the Pipeline\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipeline\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTrainingPipeline\u003c/span\u003e(\n... \t\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_training_config\u003c/span\u003e,\n... \t\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_vae_model\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Launch the Pipeline\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epipeline\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eyour_train_data\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# must be torch.Tensor, np.array or torch datasets\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003eeval_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eyour_eval_data\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# must be torch.Tensor, np.array or torch datasets\u003c/span\u003e\n... )\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAt the end of training, the best model weights, model configuration and training configuration are stored in a \u003ccode\u003efinal_model\u003c/code\u003e folder available in \u003ccode\u003emy_model/MODEL_NAME_training_YYYY-MM-DD_hh-mm-ss\u003c/code\u003e (with \u003ccode\u003emy_model\u003c/code\u003e being the \u003ccode\u003eoutput_dir\u003c/code\u003e argument of the \u003ccode\u003eBaseTrainerConfig\u003c/code\u003e). If you further set the \u003ccode\u003esteps_saving\u003c/code\u003e argument to a certain value, folders named \u003ccode\u003echeckpoint_epoch_k\u003c/code\u003e containing the best model weights, optimizer, scheduler, configuration and training configuration at epoch \u003cem\u003ek\u003c/em\u003e will also appear in \u003ccode\u003emy_model/MODEL_NAME_training_YYYY-MM-DD_hh-mm-ss\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLaunching a training on benchmark datasets\u003c/h2\u003e\u003ca id=\"user-content-launching-a-training-on-benchmark-datasets\" class=\"anchor\" aria-label=\"Permalink: Launching a training on benchmark datasets\" href=\"#launching-a-training-on-benchmark-datasets\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe also provide a training script example \u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts/training.py\"\u003ehere\u003c/a\u003e that can be used to train the models on benchmarks datasets (mnist, cifar10, celeba ...). The script can be launched with the following commandline\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python training.py --dataset mnist --model_name ae --model_config 'configs/ae_config.json' --training_config 'configs/base_training_config.json'\"\u003e\u003cpre\u003epython training.py --dataset mnist --model_name ae --model_config \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003econfigs/ae_config.json\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e --training_config \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003econfigs/base_training_config.json\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee \u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts/README.md\"\u003eREADME.md\u003c/a\u003e for further details on this script\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLaunching data generation\u003c/h2\u003e\u003ca id=\"user-content-launching-data-generation\" class=\"anchor\" aria-label=\"Permalink: Launching data generation\" href=\"#launching-data-generation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing the \u003ccode\u003eGenerationPipeline\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-using-the-generationpipeline\" class=\"anchor\" aria-label=\"Permalink: Using the GenerationPipeline\" href=\"#using-the-generationpipeline\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe easiest way to launch a data generation from a trained model consists in using the built-in \u003ccode\u003eGenerationPipeline\u003c/code\u003e provided in Pythae. Say you want to generate 100 samples using a \u003ccode\u003eMAFSampler\u003c/code\u003e all you have to do is 1) relaod the trained model, 2) define the sampler's configuration and 3) create and launch the \u003ccode\u003eGenerationPipeline\u003c/code\u003e as follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from pythae.models import AutoModel\n\u0026gt;\u0026gt;\u0026gt; from pythae.samplers import MAFSamplerConfig\n\u0026gt;\u0026gt;\u0026gt; from pythae.pipelines import GenerationPipeline\n\u0026gt;\u0026gt;\u0026gt; # Retrieve the trained model\n\u0026gt;\u0026gt;\u0026gt; my_trained_vae = AutoModel.load_from_folder(\n...\t'path/to/your/trained/model'\n... )\n\u0026gt;\u0026gt;\u0026gt; my_sampler_config = MAFSamplerConfig(\n...\tn_made_blocks=2,\n...\tn_hidden_in_made=3,\n...\thidden_size=128\n... )\n\u0026gt;\u0026gt;\u0026gt; # Build the pipeline\n\u0026gt;\u0026gt;\u0026gt; pipe = GenerationPipeline(\n...\tmodel=my_trained_vae,\n...\tsampler_config=my_sampler_config\n... )\n\u0026gt;\u0026gt;\u0026gt; # Launch data generation\n\u0026gt;\u0026gt;\u0026gt; generated_samples = pipe(\n...\tnum_samples=args.num_samples,\n...\treturn_gen=True, # If false returns nothing\n...\ttrain_data=train_data, # Needed to fit the sampler\n...\teval_data=eval_data, # Needed to fit the sampler\n...\ttraining_config=BaseTrainerConfig(num_epochs=200) # TrainingConfig to use to fit the sampler\n... )\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003esamplers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMAFSamplerConfig\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epipelines\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGenerationPipeline\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Retrieve the trained model\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_trained_vae\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_from_folder\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s\"\u003e'path/to/your/trained/model'\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_sampler_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMAFSamplerConfig\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003en_made_blocks\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003en_hidden_in_made\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ehidden_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e128\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Build the pipeline\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipe\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGenerationPipeline\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_trained_vae\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003esampler_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_sampler_config\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Launch data generation\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egenerated_samples\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epipe\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003enum_samples\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enum_samples\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ereturn_gen\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# If false returns nothing\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# Needed to fit the sampler\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003eeval_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eeval_data\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# Needed to fit the sampler\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-v\"\u003eBaseTrainerConfig\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enum_epochs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e200\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# TrainingConfig to use to fit the sampler\u003c/span\u003e\n... )\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing the Samplers\u003c/h3\u003e\u003ca id=\"user-content-using-the-samplers\" class=\"anchor\" aria-label=\"Permalink: Using the Samplers\" href=\"#using-the-samplers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAlternatively, you can launch the data generation process from a trained model directly with the sampler. For instance, to generate new data with your sampler, run the following.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from pythae.models import AutoModel\n\u0026gt;\u0026gt;\u0026gt; from pythae.samplers import NormalSampler\n\u0026gt;\u0026gt;\u0026gt; # Retrieve the trained model\n\u0026gt;\u0026gt;\u0026gt; my_trained_vae = AutoModel.load_from_folder(\n...\t'path/to/your/trained/model'\n... )\n\u0026gt;\u0026gt;\u0026gt; # Define your sampler\n\u0026gt;\u0026gt;\u0026gt; my_samper = NormalSampler(\n...\tmodel=my_trained_vae\n... )\n\u0026gt;\u0026gt;\u0026gt; # Generate samples\n\u0026gt;\u0026gt;\u0026gt; gen_data = my_samper.sample(\n...\tnum_samples=50,\n...\tbatch_size=10,\n...\toutput_dir=None,\n...\treturn_gen=True\n... )\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003esamplers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNormalSampler\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Retrieve the trained model\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_trained_vae\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_from_folder\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s\"\u003e'path/to/your/trained/model'\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Define your sampler\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_samper\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNormalSampler\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_trained_vae\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Generate samples\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egen_data\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_samper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esample\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003enum_samples\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ebatch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eoutput_dir\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ereturn_gen\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e\n... )\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you set \u003ccode\u003eoutput_dir\u003c/code\u003e to a specific path, the generated images will be saved as \u003ccode\u003e.png\u003c/code\u003e files named \u003ccode\u003e00000000.png\u003c/code\u003e, \u003ccode\u003e00000001.png\u003c/code\u003e ...\nThe samplers can be used with any model as long as it is suited. For instance, a \u003ccode\u003eGaussianMixtureSampler\u003c/code\u003e instance can be used to generate from any model but a \u003ccode\u003eVAMPSampler\u003c/code\u003e will only be usable with a \u003ccode\u003eVAMP\u003c/code\u003e model. Check \u003ca href=\"#available-samplers\"\u003ehere\u003c/a\u003e to see which ones apply to your model. Be carefull that some samplers such as the \u003ccode\u003eGaussianMixtureSampler\u003c/code\u003e for instance may need to be fitted by calling the \u003ccode\u003efit\u003c/code\u003e method before using. Below is an example for the \u003ccode\u003eGaussianMixtureSampler\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from pythae.models import AutoModel\n\u0026gt;\u0026gt;\u0026gt; from pythae.samplers import GaussianMixtureSampler, GaussianMixtureSamplerConfig\n\u0026gt;\u0026gt;\u0026gt; # Retrieve the trained model\n\u0026gt;\u0026gt;\u0026gt; my_trained_vae = AutoModel.load_from_folder(\n...\t'path/to/your/trained/model'\n... )\n\u0026gt;\u0026gt;\u0026gt; # Define your sampler\n... gmm_sampler_config = GaussianMixtureSamplerConfig(\n...\tn_components=10\n... )\n\u0026gt;\u0026gt;\u0026gt; my_samper = GaussianMixtureSampler(\n...\tsampler_config=gmm_sampler_config,\n...\tmodel=my_trained_vae\n... )\n\u0026gt;\u0026gt;\u0026gt; # fit the sampler\n\u0026gt;\u0026gt;\u0026gt; gmm_sampler.fit(train_dataset)\n\u0026gt;\u0026gt;\u0026gt; # Generate samples\n\u0026gt;\u0026gt;\u0026gt; gen_data = my_samper.sample(\n...\tnum_samples=50,\n...\tbatch_size=10,\n...\toutput_dir=None,\n...\treturn_gen=True\n... )\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003esamplers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGaussianMixtureSampler\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eGaussianMixtureSamplerConfig\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Retrieve the trained model\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_trained_vae\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_from_folder\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s\"\u003e'path/to/your/trained/model'\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Define your sampler\u003c/span\u003e\n... \u003cspan class=\"pl-s1\"\u003egmm_sampler_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGaussianMixtureSamplerConfig\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003en_components\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_samper\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGaussianMixtureSampler\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003esampler_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003egmm_sampler_config\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_trained_vae\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# fit the sampler\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egmm_sampler\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Generate samples\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003egen_data\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_samper\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esample\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003enum_samples\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ebatch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eoutput_dir\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ereturn_gen\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e\n... )\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDefine you own Autoencoder architecture\u003c/h2\u003e\u003ca id=\"user-content-define-you-own-autoencoder-architecture\" class=\"anchor\" aria-label=\"Permalink: Define you own Autoencoder architecture\" href=\"#define-you-own-autoencoder-architecture\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePythae provides you the possibility to define your own neural networks within the VAE models. For instance, say you want to train a Wassertstein AE with a specific encoder and decoder, you can do the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from pythae.models.nn import BaseEncoder, BaseDecoder\n\u0026gt;\u0026gt;\u0026gt; from pythae.models.base.base_utils import ModelOutput\n\u0026gt;\u0026gt;\u0026gt; class My_Encoder(BaseEncoder):\n...\tdef __init__(self, args=None): # Args is a ModelConfig instance\n...\t\tBaseEncoder.__init__(self)\n...\t\tself.layers = my_nn_layers()\n...\t\t\n...\tdef forward(self, x:torch.Tensor) -\u0026gt; ModelOutput:\n...\t\tout = self.layers(x)\n...\t\toutput = ModelOutput(\n...\t\t\tembedding=out # Set the output from the encoder in a ModelOutput instance \n...\t\t)\n...\t\treturn output\n...\n... class My_Decoder(BaseDecoder):\n...\tdef __init__(self, args=None):\n...\t\tBaseDecoder.__init__(self)\n...\t\tself.layers = my_nn_layers()\n...\t\t\n...\tdef forward(self, x:torch.Tensor) -\u0026gt; ModelOutput:\n...\t\tout = self.layers(x)\n...\t\toutput = ModelOutput(\n...\t\t\treconstruction=out # Set the output from the decoder in a ModelOutput instance\n...\t\t)\n...\t\treturn output\n...\n\u0026gt;\u0026gt;\u0026gt; my_encoder = My_Encoder()\n\u0026gt;\u0026gt;\u0026gt; my_decoder = My_Decoder()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enn\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBaseEncoder\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eBaseDecoder\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ebase\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ebase_utils\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eModelOutput\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMy_Encoder\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eBaseEncoder\u003c/span\u003e):\n...\t\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e__init__\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e): \u003cspan class=\"pl-c\"\u003e# Args is a ModelConfig instance\u003c/span\u003e\n...\t\t\u003cspan class=\"pl-v\"\u003eBaseEncoder\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003e__init__\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e)\n...\t\t\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayers\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emy_nn_layers\u003c/span\u003e()\n...\t\t\n...\t\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eforward\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e:\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eTensor\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eModelOutput\u003c/span\u003e:\n...\t\t\u003cspan class=\"pl-s1\"\u003eout\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003elayers\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e)\n...\t\t\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eModelOutput\u003c/span\u003e(\n...\t\t\t\u003cspan class=\"pl-s1\"\u003eembedding\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eout\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Set the output from the encoder in a ModelOutput instance \u003c/span\u003e\n...\t\t)\n...\t\t\u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e\n...\n... \u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMy_Decoder\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eBaseDecoder\u003c/span\u003e):\n...\t\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e__init__\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eargs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e):\n...\t\t\u003cspan class=\"pl-v\"\u003eBaseDecoder\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003e__init__\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e)\n...\t\t\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayers\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emy_nn_layers\u003c/span\u003e()\n...\t\t\n...\t\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eforward\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e:\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eTensor\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eModelOutput\u003c/span\u003e:\n...\t\t\u003cspan class=\"pl-s1\"\u003eout\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003elayers\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e)\n...\t\t\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eModelOutput\u003c/span\u003e(\n...\t\t\t\u003cspan class=\"pl-s1\"\u003ereconstruction\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eout\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Set the output from the decoder in a ModelOutput instance\u003c/span\u003e\n...\t\t)\n...\t\t\u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e\n...\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_encoder\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMy_Encoder\u003c/span\u003e()\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_decoder\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMy_Decoder\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAnd now build the model\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from pythae.models import WAE_MMD, WAE_MMD_Config\n\u0026gt;\u0026gt;\u0026gt; # Set up the model configuration \n\u0026gt;\u0026gt;\u0026gt; my_wae_config = model_config = WAE_MMD_Config(\n...\tinput_dim=(1, 28, 28),\n...\tlatent_dim=10\n... )\n...\n\u0026gt;\u0026gt;\u0026gt; # Build the model\n\u0026gt;\u0026gt;\u0026gt; my_wae_model = WAE_MMD(\n...\tmodel_config=my_wae_config,\n...\tencoder=my_encoder, # pass your encoder as argument when building the model\n...\tdecoder=my_decoder # pass your decoder as argument when building the model\n... )\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eWAE_MMD\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eWAE_MMD_Config\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Set up the model configuration \u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_wae_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eWAE_MMD_Config\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003einput_dim\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e28\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e28\u003c/span\u003e),\n...\t\u003cspan class=\"pl-s1\"\u003elatent_dim\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e\n... )\n...\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Build the model\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_wae_model\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eWAE_MMD\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003emodel_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_wae_config\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eencoder\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_encoder\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# pass your encoder as argument when building the model\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003edecoder\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emy_decoder\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# pass your decoder as argument when building the model\u003c/span\u003e\n... )\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eimportant note 1\u003c/strong\u003e: For all AE-based models (AE, WAE, RAE_L2, RAE_GP), both the encoder and decoder must return a \u003ccode\u003eModelOutput\u003c/code\u003e instance. For the encoder, the \u003ccode\u003eModelOutput\u003c/code\u003e instance must contain the embbeddings under the key \u003ccode\u003eembedding\u003c/code\u003e. For the decoder, the \u003ccode\u003eModelOutput\u003c/code\u003e instance must contain the reconstructions under the key \u003ccode\u003ereconstruction\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eimportant note 2\u003c/strong\u003e: For all VAE-based models (VAE, BetaVAE, IWAE, HVAE, VAMP, RHVAE), both the encoder and decoder must return a \u003ccode\u003eModelOutput\u003c/code\u003e instance. For the encoder, the \u003ccode\u003eModelOutput\u003c/code\u003e instance must contain the embbeddings and \u003cstrong\u003elog\u003c/strong\u003e-covariance matrices (of shape batch_size x latent_space_dim) respectively under the key \u003ccode\u003eembedding\u003c/code\u003e and \u003ccode\u003elog_covariance\u003c/code\u003e key. For the decoder, the \u003ccode\u003eModelOutput\u003c/code\u003e instance must contain the reconstructions under the key \u003ccode\u003ereconstruction\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing benchmark neural nets\u003c/h2\u003e\u003ca id=\"user-content-using-benchmark-neural-nets\" class=\"anchor\" aria-label=\"Permalink: Using benchmark neural nets\" href=\"#using-benchmark-neural-nets\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can also find predefined neural network architectures for the most common data sets (\u003cem\u003ei.e.\u003c/em\u003e MNIST, CIFAR, CELEBA ...) that can be loaded as follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from pythae.models.nn.benchmark.mnist import (\n...\tEncoder_Conv_AE_MNIST, # For AE based model (only return embeddings)\n...\tEncoder_Conv_VAE_MNIST, # For VAE based model (return embeddings and log_covariances)\n...\tDecoder_Conv_AE_MNIST\n... )\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ebenchmark\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emnist\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e (\n...\t\u003cspan class=\"pl-v\"\u003eEncoder_Conv_AE_MNIST\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# For AE based model (only return embeddings)\u003c/span\u003e\n...\t\u003cspan class=\"pl-v\"\u003eEncoder_Conv_VAE_MNIST\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# For VAE based model (return embeddings and log_covariances)\u003c/span\u003e\n...\t\u003cspan class=\"pl-v\"\u003eDecoder_Conv_AE_MNIST\u003c/span\u003e\n... )\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eReplace \u003cem\u003emnist\u003c/em\u003e by cifar or celeba to access to other neural nets.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDistributed Training with \u003ccode\u003ePythae\u003c/code\u003e\u003c/h2\u003e\u003ca id=\"user-content-distributed-training-with-pythae\" class=\"anchor\" aria-label=\"Permalink: Distributed Training with Pythae\" href=\"#distributed-training-with-pythae\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAs of \u003ccode\u003ev0.1.0\u003c/code\u003e, Pythae now supports distributed training using PyTorch's \u003ca href=\"https://pytorch.org/docs/stable/notes/ddp.html\" rel=\"nofollow\"\u003eDDP\u003c/a\u003e. It allows you to train your favorite VAE faster and on larger dataset using multi-gpu and/or multi-node training.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo do so, you can build a python script that will then be launched by a launcher (such as \u003ccode\u003esrun\u003c/code\u003e on a cluster). The only thing that is needed in the script is to specify some elements relative to the distributed environment (such as the number of nodes/gpus) directly in the training configuration as follows\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; training_config = BaseTrainerConfig(\n... num_epochs=10,\n... learning_rate=1e-3,\n... per_device_train_batch_size=64,\n... per_device_eval_batch_size=64,\n... train_dataloader_num_workers=8,\n... eval_dataloader_num_workers=8,\n... dist_backend=\u0026quot;nccl\u0026quot;, # distributed backend\n... world_size=8 # number of gpus to use (n_nodes x n_gpus_per_node),\n... rank=5 # global gpu id,\n... local_rank=1 # gpu id within a node,\n... master_addr=\u0026quot;localhost\u0026quot; # master address,\n... master_port=\u0026quot;12345\u0026quot; # master port,\n... )\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBaseTrainerConfig\u003c/span\u003e(\n... \u003cspan class=\"pl-s1\"\u003enum_epochs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,\n... \u003cspan class=\"pl-s1\"\u003elearning_rate\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1e-3\u003c/span\u003e,\n... \u003cspan class=\"pl-s1\"\u003eper_device_train_batch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e64\u003c/span\u003e,\n... \u003cspan class=\"pl-s1\"\u003eper_device_eval_batch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e64\u003c/span\u003e,\n... \u003cspan class=\"pl-s1\"\u003etrain_dataloader_num_workers\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\n... \u003cspan class=\"pl-s1\"\u003eeval_dataloader_num_workers\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e,\n... \u003cspan class=\"pl-s1\"\u003edist_backend\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"nccl\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# distributed backend\u003c/span\u003e\n... \u003cspan class=\"pl-s1\"\u003eworld_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# number of gpus to use (n_nodes x n_gpus_per_node),\u003c/span\u003e\n... \u003cspan class=\"pl-s1\"\u003erank\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# global gpu id,\u003c/span\u003e\n... \u003cspan class=\"pl-s1\"\u003elocal_rank\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# gpu id within a node,\u003c/span\u003e\n... \u003cspan class=\"pl-s1\"\u003emaster_addr\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"localhost\"\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# master address,\u003c/span\u003e\n... \u003cspan class=\"pl-s1\"\u003emaster_port\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"12345\"\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# master port,\u003c/span\u003e\n... )\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee this \u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/scripts/distributed_training_imagenet.py\"\u003eexample script\u003c/a\u003e that defines a multi-gpu VQVAE training on ImageNet dataset. Please note that the way the distributed environnement variables (\u003ccode\u003eworld_size\u003c/code\u003e, \u003ccode\u003erank\u003c/code\u003e ...) are recovered may be specific to the cluster and launcher you use.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBenchmark\u003c/h3\u003e\u003ca id=\"user-content-benchmark\" class=\"anchor\" aria-label=\"Permalink: Benchmark\" href=\"#benchmark\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBelow are indicated the training times for a Vector Quantized VAE (VQ-VAE) with \u003ccode\u003ePythae\u003c/code\u003e for 100 epochs on MNIST on V100 16GB GPU(s), for 50 epochs on \u003ca href=\"https://github.com/NVlabs/ffhq-dataset\"\u003eFFHQ\u003c/a\u003e (1024x1024 images) and for 20 epochs on \u003ca href=\"https://huggingface.co/datasets/imagenet-1k\" rel=\"nofollow\"\u003eImageNet-1k\u003c/a\u003e on V100 32GB GPU(s).\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003eTrain Data\u003c/th\u003e\n\u003cth align=\"center\"\u003e1 GPU\u003c/th\u003e\n\u003cth align=\"center\"\u003e4 GPUs\u003c/th\u003e\n\u003cth\u003e2x4 GPUs\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eMNIST (VQ-VAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e28x28 images (50k)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e235.18 s\u003c/td\u003e\n\u003ctd align=\"center\"\u003e62.00 s\u003c/td\u003e\n\u003ctd\u003e35.86 s\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eFFHQ 1024x1024 (VQVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1024x1024 RGB images (60k)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e19h 1min\u003c/td\u003e\n\u003ctd align=\"center\"\u003e5h 6min\u003c/td\u003e\n\u003ctd\u003e2h 37min\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eImageNet-1k 128x128 (VQVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e128x128 RGB images (~ 1.2M)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e6h 25min\u003c/td\u003e\n\u003ctd align=\"center\"\u003e1h 41min\u003c/td\u003e\n\u003ctd\u003e51min 26s\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eFor each dataset, we provide the benchmarking scripts \u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSharing your models with the HuggingFace Hub 🤗\u003c/h2\u003e\u003ca id=\"user-content-sharing-your-models-with-the-huggingface-hub-\" class=\"anchor\" aria-label=\"Permalink: Sharing your models with the HuggingFace Hub 🤗\" href=\"#sharing-your-models-with-the-huggingface-hub-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePythae also allows you to share your models on the \u003ca href=\"https://huggingface.co/models\" rel=\"nofollow\"\u003eHuggingFace Hub\u003c/a\u003e. To do so you need:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ea valid HuggingFace account\u003c/li\u003e\n\u003cli\u003ethe package \u003ccode\u003ehuggingface_hub\u003c/code\u003e installed in your virtual env. If not you can install it with\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ python -m pip install huggingface_hub\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ python -m pip install huggingface_hub\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eto be logged in to your HuggingFace account using\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ huggingface-cli login\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ huggingface-cli login\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUploading a model to the Hub\u003c/h3\u003e\u003ca id=\"user-content-uploading-a-model-to-the-hub\" class=\"anchor\" aria-label=\"Permalink: Uploading a model to the Hub\" href=\"#uploading-a-model-to-the-hub\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAny pythae model can be easily uploaded using the method \u003ccode\u003epush_to_hf_hub\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; my_vae_model.push_to_hf_hub(hf_hub_path=\u0026quot;your_hf_username/your_hf_hub_repo\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_vae_model\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003epush_to_hf_hub\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehf_hub_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"your_hf_username/your_hf_hub_repo\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e If \u003ccode\u003eyour_hf_hub_repo\u003c/code\u003e already exists and is not empty, files will be overridden. In case,\nthe repo \u003ccode\u003eyour_hf_hub_repo\u003c/code\u003e does not exist, a folder having the same name will be created.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDownloading models from the Hub\u003c/h3\u003e\u003ca id=\"user-content-downloading-models-from-the-hub\" class=\"anchor\" aria-label=\"Permalink: Downloading models from the Hub\" href=\"#downloading-models-from-the-hub\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEquivalently, you can download or reload any Pythae's model directly from the Hub using the method \u003ccode\u003eload_from_hf_hub\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; from pythae.models import AutoModel\n\u0026gt;\u0026gt;\u0026gt; my_downloaded_vae = AutoModel.load_from_hf_hub(hf_hub_path=\u0026quot;path_to_hf_repo\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emy_downloaded_vae\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoModel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_from_hf_hub\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehf_hub_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"path_to_hf_repo\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMonitoring your experiments with \u003ccode\u003ewandb\u003c/code\u003e 🧪\u003c/h2\u003e\u003ca id=\"user-content-monitoring-your-experiments-with-wandb-\" class=\"anchor\" aria-label=\"Permalink: Monitoring your experiments with wandb 🧪\" href=\"#monitoring-your-experiments-with-wandb-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePythae also integrates the experiment tracking tool \u003ca href=\"https://wandb.ai/\" rel=\"nofollow\"\u003ewandb\u003c/a\u003e allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ea valid wandb account\u003c/li\u003e\n\u003cli\u003ethe package \u003ccode\u003ewandb\u003c/code\u003e installed in your virtual env. If not you can install it with\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ pip install wandb\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ pip install wandb\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eto be logged in to your wandb account using\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ wandb login\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ wandb login\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCreating a \u003ccode\u003eWandbCallback\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-creating-a-wandbcallback\" class=\"anchor\" aria-label=\"Permalink: Creating a WandbCallback\" href=\"#creating-a-wandbcallback\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eLaunching an experiment monitoring with \u003ccode\u003ewandb\u003c/code\u003e in pythae is pretty simple. The only thing a user needs to do is create a \u003ccode\u003eWandbCallback\u003c/code\u003e instance...\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; # Create you callback\n\u0026gt;\u0026gt;\u0026gt; from pythae.trainers.training_callbacks import WandbCallback\n\u0026gt;\u0026gt;\u0026gt; callbacks = [] # the TrainingPipeline expects a list of callbacks\n\u0026gt;\u0026gt;\u0026gt; wandb_cb = WandbCallback() # Build the callback \n\u0026gt;\u0026gt;\u0026gt; # SetUp the callback \n\u0026gt;\u0026gt;\u0026gt; wandb_cb.setup(\n...\ttraining_config=your_training_config, # training config\n...\tmodel_config=your_model_config, # model config\n...\tproject_name=\u0026quot;your_wandb_project\u0026quot;, # specify your wandb project\n...\tentity_name=\u0026quot;your_wandb_entity\u0026quot;, # specify your wandb entity\n... )\n\u0026gt;\u0026gt;\u0026gt; callbacks.append(wandb_cb) # Add it to the callbacks list\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Create you callback\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etrainers\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etraining_callbacks\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eWandbCallback\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [] \u003cspan class=\"pl-c\"\u003e# the TrainingPipeline expects a list of callbacks\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ewandb_cb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eWandbCallback\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# Build the callback \u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# SetUp the callback \u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ewandb_cb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esetup\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eyour_training_config\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# training config\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003emodel_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eyour_model_config\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# model config\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003eproject_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"your_wandb_project\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# specify your wandb project\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003eentity_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"your_wandb_entity\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# specify your wandb entity\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewandb_cb\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# Add it to the callbacks list\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e...and then pass it to the \u003ccode\u003eTrainingPipeline\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; pipeline = TrainingPipeline(\n...\ttraining_config=config,\n...\tmodel=model\n... )\n\u0026gt;\u0026gt;\u0026gt; pipeline(\n...\ttrain_data=train_dataset,\n...\teval_data=eval_dataset,\n...\tcallbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done!\n... )\n\u0026gt;\u0026gt;\u0026gt; # You can log to https://wandb.ai/your_wandb_entity/your_wandb_project to monitor your training\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipeline\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTrainingPipeline\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epipeline\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eeval_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eeval_dataset\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# pass the callbacks to the TrainingPipeline and you are done!\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# You can log to https://wandb.ai/your_wandb_entity/your_wandb_project to monitor your training\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee the detailed tutorial\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMonitoring your experiments with \u003ccode\u003emlflow\u003c/code\u003e 🧪\u003c/h2\u003e\u003ca id=\"user-content-monitoring-your-experiments-with-mlflow-\" class=\"anchor\" aria-label=\"Permalink: Monitoring your experiments with mlflow 🧪\" href=\"#monitoring-your-experiments-with-mlflow-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePythae also integrates the experiment tracking tool \u003ca href=\"https://mlflow.org/\" rel=\"nofollow\"\u003emlflow\u003c/a\u003e allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ethe package \u003ccode\u003emlfow\u003c/code\u003e installed in your virtual env. If not you can install it with\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ pip install mlflow\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ pip install mlflow\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCreating a \u003ccode\u003eMLFlowCallback\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-creating-a-mlflowcallback\" class=\"anchor\" aria-label=\"Permalink: Creating a MLFlowCallback\" href=\"#creating-a-mlflowcallback\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eLaunching an experiment monitoring with \u003ccode\u003emlfow\u003c/code\u003e in pythae is pretty simple. The only thing a user needs to do is create a \u003ccode\u003eMLFlowCallback\u003c/code\u003e instance...\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; # Create you callback\n\u0026gt;\u0026gt;\u0026gt; from pythae.trainers.training_callbacks import MLFlowCallback\n\u0026gt;\u0026gt;\u0026gt; callbacks = [] # the TrainingPipeline expects a list of callbacks\n\u0026gt;\u0026gt;\u0026gt; mlflow_cb = MLFlowCallback() # Build the callback \n\u0026gt;\u0026gt;\u0026gt; # SetUp the callback \n\u0026gt;\u0026gt;\u0026gt; mlflow_cb.setup(\n...\ttraining_config=your_training_config, # training config\n...\tmodel_config=your_model_config, # model config\n...\trun_name=\u0026quot;mlflow_cb_example\u0026quot;, # specify your mlflow run\n... )\n\u0026gt;\u0026gt;\u0026gt; callbacks.append(mlflow_cb) # Add it to the callbacks list\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Create you callback\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etrainers\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etraining_callbacks\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMLFlowCallback\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [] \u003cspan class=\"pl-c\"\u003e# the TrainingPipeline expects a list of callbacks\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emlflow_cb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMLFlowCallback\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# Build the callback \u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# SetUp the callback \u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emlflow_cb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esetup\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eyour_training_config\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# training config\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003emodel_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eyour_model_config\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# model config\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003erun_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"mlflow_cb_example\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# specify your mlflow run\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emlflow_cb\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# Add it to the callbacks list\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e...and then pass it to the \u003ccode\u003eTrainingPipeline\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; pipeline = TrainingPipeline(\n...\ttraining_config=config,\n...\tmodel=model\n... )\n\u0026gt;\u0026gt;\u0026gt; pipeline(\n...\ttrain_data=train_dataset,\n...\teval_data=eval_dataset,\n...\tcallbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done!\n... )\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipeline\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTrainingPipeline\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epipeline\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eeval_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eeval_dataset\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# pass the callbacks to the TrainingPipeline and you are done!\u003c/span\u003e\n... )\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eyou can visualize your metric by running the following in the directory where the \u003ccode\u003e./mlruns\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ mlflow ui \"\u003e\u003cpre\u003e$ mlflow ui \u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee the detailed tutorial\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMonitoring your experiments with \u003ccode\u003ecomet_ml\u003c/code\u003e 🧪\u003c/h2\u003e\u003ca id=\"user-content-monitoring-your-experiments-with-comet_ml-\" class=\"anchor\" aria-label=\"Permalink: Monitoring your experiments with comet_ml 🧪\" href=\"#monitoring-your-experiments-with-comet_ml-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePythae also integrates the experiment tracking tool \u003ca href=\"https://www.comet.com/signup?utm_source=pythae\u0026amp;utm_medium=partner\u0026amp;utm_campaign=AMS_US_EN_SNUP_Pythae_Comet_Integration\" rel=\"nofollow\"\u003ecomet_ml\u003c/a\u003e allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ethe package \u003ccode\u003ecomet_ml\u003c/code\u003e installed in your virtual env. If not you can install it with\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ pip install comet_ml\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ pip install comet_ml\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCreating a \u003ccode\u003eCometCallback\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-creating-a-cometcallback\" class=\"anchor\" aria-label=\"Permalink: Creating a CometCallback\" href=\"#creating-a-cometcallback\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eLaunching an experiment monitoring with \u003ccode\u003ecomet_ml\u003c/code\u003e in pythae is pretty simple. The only thing a user needs to do is create a \u003ccode\u003eCometCallback\u003c/code\u003e instance...\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; # Create you callback\n\u0026gt;\u0026gt;\u0026gt; from pythae.trainers.training_callbacks import CometCallback\n\u0026gt;\u0026gt;\u0026gt; callbacks = [] # the TrainingPipeline expects a list of callbacks\n\u0026gt;\u0026gt;\u0026gt; comet_cb = CometCallback() # Build the callback \n\u0026gt;\u0026gt;\u0026gt; # SetUp the callback \n\u0026gt;\u0026gt;\u0026gt; comet_cb.setup(\n...\ttraining_config=training_config, # training config\n...\tmodel_config=model_config, # model config\n...\tapi_key=\u0026quot;your_comet_api_key\u0026quot;, # specify your comet api-key\n...\tproject_name=\u0026quot;your_comet_project\u0026quot;, # specify your wandb project\n...\t#offline_run=True, # run in offline mode\n...\t#offline_directory='my_offline_runs' # set the directory to store the offline runs\n... )\n\u0026gt;\u0026gt;\u0026gt; callbacks.append(comet_cb) # Add it to the callbacks list\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# Create you callback\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epythae\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etrainers\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etraining_callbacks\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eCometCallback\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [] \u003cspan class=\"pl-c\"\u003e# the TrainingPipeline expects a list of callbacks\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecomet_cb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eCometCallback\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# Build the callback \u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# SetUp the callback \u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecomet_cb\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esetup\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# training config\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003emodel_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emodel_config\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# model config\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003eapi_key\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"your_comet_api_key\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# specify your comet api-key\u003c/span\u003e\n...\t\u003cspan class=\"pl-s1\"\u003eproject_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"your_comet_project\"\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# specify your wandb project\u003c/span\u003e\n...\t\u003cspan class=\"pl-c\"\u003e#offline_run=True, # run in offline mode\u003c/span\u003e\n...\t\u003cspan class=\"pl-c\"\u003e#offline_directory='my_offline_runs' # set the directory to store the offline runs\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecomet_cb\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# Add it to the callbacks list\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e...and then pass it to the \u003ccode\u003eTrainingPipeline\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"\u0026gt;\u0026gt;\u0026gt; pipeline = TrainingPipeline(\n...\ttraining_config=config,\n...\tmodel=model\n... )\n\u0026gt;\u0026gt;\u0026gt; pipeline(\n...\ttrain_data=train_dataset,\n...\teval_data=eval_dataset,\n...\tcallbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done!\n... )\n\u0026gt;\u0026gt;\u0026gt; # You can log to https://comet.com/your_comet_username/your_comet_project to monitor your training\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipeline\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTrainingPipeline\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etraining_config\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-en\"\u003epipeline\u003c/span\u003e(\n...\t\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_dataset\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003eeval_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eeval_dataset\u003c/span\u003e,\n...\t\u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ecallbacks\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# pass the callbacks to the TrainingPipeline and you are done!\u003c/span\u003e\n... )\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# You can log to https://comet.com/your_comet_username/your_comet_project to monitor your training\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee the detailed tutorial\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting your hands on the code\u003c/h2\u003e\u003ca id=\"user-content-getting-your-hands-on-the-code\" class=\"anchor\" aria-label=\"Permalink: Getting your hands on the code\" href=\"#getting-your-hands-on-the-code\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo help you to understand the way pythae works and how you can train your models with this library we also\nprovide tutorials:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks\"\u003emaking_your_own_autoencoder.ipynb\u003c/a\u003e shows you how to pass your own networks to the models implemented in pythae \u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/making_your_own_autoencoder.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks\"\u003ecustom_dataset.ipynb\u003c/a\u003e shows you how to use custom datasets with any of the models implemented in pythae \u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/custom_dataset.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks\"\u003ehf_hub_models_sharing.ipynb\u003c/a\u003e shows you how to upload and download models for the HuggingFace Hub \u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/hf_hub_models_sharing.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks\"\u003ewandb_experiment_monitoring.ipynb\u003c/a\u003e shows you how to monitor you experiments using \u003ccode\u003ewandb\u003c/code\u003e \u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/wandb_experiment_monitoring.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks\"\u003emlflow_experiment_monitoring.ipynb\u003c/a\u003e shows you how to monitor you experiments using \u003ccode\u003emlflow\u003c/code\u003e \u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/mlflow_experiment_monitoring.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks\"\u003ecomet_experiment_monitoring.ipynb\u003c/a\u003e shows you how to monitor you experiments using \u003ccode\u003ecomet_ml\u003c/code\u003e \u003ca href=\"https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/comet_experiment_monitoring.ipynb\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open In Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks/models_training\"\u003emodels_training\u003c/a\u003e folder provides notebooks showing how to train each implemented model and how to sample from it using \u003ccode\u003epythae.samplers\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts\"\u003escripts\u003c/a\u003e folder provides in particular an example of a training script to train the models on benchmark data sets (mnist, cifar10, celeba ...)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDealing with issues 🛠️\u003c/h2\u003e\u003ca id=\"user-content-dealing-with-issues-️\" class=\"anchor\" aria-label=\"Permalink: Dealing with issues 🛠️\" href=\"#dealing-with-issues-️\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you are experiencing any issues while running the code or request new features/models to be implemented please \u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/issues\"\u003eopen an issue on github\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributing 🚀\u003c/h2\u003e\u003ca id=\"user-content-contributing-\" class=\"anchor\" aria-label=\"Permalink: Contributing 🚀\" href=\"#contributing-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou want to contribute to this library by adding a model, a sampler or simply fix a bug ? That's awesome! Thank you! Please see \u003ca href=\"https://github.com/clementchadebec/benchmark_VAE/tree/main/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e to follow the main contributing guidelines.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResults\u003c/h2\u003e\u003ca id=\"user-content-results\" class=\"anchor\" aria-label=\"Permalink: Results\" href=\"#results\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReconstruction\u003c/h3\u003e\u003ca id=\"user-content-reconstruction\" class=\"anchor\" aria-label=\"Permalink: Reconstruction\" href=\"#reconstruction\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFirst let's have a look at the reconstructed samples taken from the evaluation set.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eModels\u003c/th\u003e\n\u003cth align=\"center\"\u003eMNIST\u003c/th\u003e\n\u003cth align=\"center\"\u003eCELEBA\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eEval data\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/eval_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/eval_reconstruction_mnist.png\" alt=\"Eval\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/eval_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/eval_reconstruction_celeba.png\" alt=\"AE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/ae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/ae_reconstruction_mnist.png\" alt=\"AE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/ae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/ae_reconstruction_celeba.png\" alt=\"AE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_reconstruction_mnist.png\" alt=\"VAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_reconstruction_celeba.png\" alt=\"VAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBeta-VAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_vae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_vae_reconstruction_mnist.png\" alt=\"Beta\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_vae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_vae_reconstruction_celeba.png\" alt=\"Beta Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE Lin NF\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_lin_nf_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_lin_nf_reconstruction_mnist.png\" alt=\"VAE_LinNF\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_lin_nf_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_lin_nf_reconstruction_celeba.png\" alt=\"VAE_IAF Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE IAF\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_iaf_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_iaf_reconstruction_mnist.png\" alt=\"VAE_IAF\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_iaf_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_iaf_reconstruction_celeba.png\" alt=\"VAE_IAF Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eDisentangled Beta-VAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/disentangled_beta_vae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/disentangled_beta_vae_reconstruction_mnist.png\" alt=\"Disentangled Beta\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/disentangled_beta_vae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/disentangled_beta_vae_reconstruction_celeba.png\" alt=\"Disentangled Beta\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eFactorVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/factor_vae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/factor_vae_reconstruction_mnist.png\" alt=\"FactorVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/factor_vae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/factor_vae_reconstruction_celeba.png\" alt=\"FactorVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBetaTCVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_tc_vae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_tc_vae_reconstruction_mnist.png\" alt=\"BetaTCVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_tc_vae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_tc_vae_reconstruction_celeba.png\" alt=\"BetaTCVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eIWAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/iwae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/iwae_reconstruction_mnist.png\" alt=\"IWAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/iwae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/iwae_reconstruction_celeba.png\" alt=\"IWAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eMSSSIM_VAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/msssim_vae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/msssim_vae_reconstruction_mnist.png\" alt=\"MSSSIM VAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/msssim_vae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/msssim_vae_reconstruction_celeba.png\" alt=\"MSSSIM VAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eWAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/wae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/wae_reconstruction_mnist.png\" alt=\"WAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/wae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/wae_reconstruction_celeba.png\" alt=\"WAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eINFO VAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/infovae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/infovae_reconstruction_mnist.png\" alt=\"INFO\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/infovae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/infovae_reconstruction_celeba.png\" alt=\"INFO\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAMP\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vamp_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vamp_reconstruction_mnist.png\" alt=\"VAMP\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vamp_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vamp_reconstruction_celeba.png\" alt=\"VAMP\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eSVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/svae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/svae_reconstruction_mnist.png\" alt=\"SVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/svae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/svae_reconstruction_celeba.png\" alt=\"SVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eAdversarial_AE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/aae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/aae_reconstruction_mnist.png\" alt=\"AAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/aae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/aae_reconstruction_celeba.png\" alt=\"AAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE_GAN\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vaegan_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vaegan_reconstruction_mnist.png\" alt=\"VAEGAN\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vaegan_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vaegan_reconstruction_celeba.png\" alt=\"VAEGAN\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVQVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vqvae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vqvae_reconstruction_mnist.png\" alt=\"VQVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vqvae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vqvae_reconstruction_celeba.png\" alt=\"VQVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHVAE\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/hvae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/hvae_reconstruction_mnist.png\" alt=\"HVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/hvae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/hvae_reconstruction_celeba.png\" alt=\"HVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRAE_L2\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_l2_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_l2_reconstruction_mnist.png\" alt=\"RAE L2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_l2_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_l2_reconstruction_celeba.png\" alt=\"RAE L2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRAE_GP\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_gp_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_gp_reconstruction_mnist.png\" alt=\"RAE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_gp_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_gp_reconstruction_celeba.png\" alt=\"RAE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRiemannian Hamiltonian VAE (RHVAE)\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rhvae_reconstruction_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rhvae_reconstruction_mnist.png\" alt=\"RHVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rhvae_reconstruction_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rhvae_reconstruction_celeba.png\" alt=\"RHVAE RHVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGeneration\u003c/h3\u003e\u003ca id=\"user-content-generation\" class=\"anchor\" aria-label=\"Permalink: Generation\" href=\"#generation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHere, we show the generated samples using each model implemented in the library and different samplers.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003eModels\u003c/th\u003e\n\u003cth align=\"center\"\u003eMNIST\u003c/th\u003e\n\u003cth align=\"center\"\u003eCELEBA\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eAE + GaussianMixtureSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/ae_gmm_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/ae_gmm_sampling_mnist.png\" alt=\"AE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/ae_gmm_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/ae_gmm_sampling_celeba.png\" alt=\"AE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_normal_sampling_mnist.png\" alt=\"VAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_normal_sampling_celeba.png\" alt=\"VAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE + GaussianMixtureSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_gmm_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_gmm_sampling_mnist.png\" alt=\"VAE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_gmm_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_gmm_sampling_celeba.png\" alt=\"VAE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE + TwoStageVAESampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_second_stage_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_second_stage_sampling_mnist.png\" alt=\"VAE 2 stage\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_second_stage_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_second_stage_sampling_celeba.png\" alt=\"VAE 2 stage\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE + MAFSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_maf_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_maf_sampling_mnist.png\" alt=\"VAE MAF\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_maf_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_maf_sampling_celeba.png\" alt=\"VAE MAF\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBeta-VAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_vae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_vae_normal_sampling_mnist.png\" alt=\"Beta Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_vae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_vae_normal_sampling_celeba.png\" alt=\"Beta Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE Lin NF + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_lin_nf_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_lin_nf_normal_sampling_mnist.png\" alt=\"VAE_LinNF Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_lin_nf_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_lin_nf_normal_sampling_celeba.png\" alt=\"VAE_LinNF Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAE IAF + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_iaf_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_iaf_normal_sampling_mnist.png\" alt=\"VAE_IAF Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_iaf_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_iaf_normal_sampling_celeba.png\" alt=\"VAE IAF Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eDisentangled Beta-VAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/disentangled_beta_vae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/disentangled_beta_vae_normal_sampling_mnist.png\" alt=\"Disentangled Beta Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/disentangled_beta_vae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/disentangled_beta_vae_normal_sampling_celeba.png\" alt=\"Disentangled Beta Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eFactorVAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/factor_vae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/factor_vae_normal_sampling_mnist.png\" alt=\"FactorVAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/factor_vae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/factor_vae_normal_sampling_celeba.png\" alt=\"FactorVAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eBetaTCVAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_tc_vae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_tc_vae_normal_sampling_mnist.png\" alt=\"BetaTCVAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_tc_vae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_tc_vae_normal_sampling_celeba.png\" alt=\"BetaTCVAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eIWAE + Normal sampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/iwae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/iwae_normal_sampling_mnist.png\" alt=\"IWAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/iwae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/iwae_normal_sampling_celeba.png\" alt=\"IWAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eMSSSIM_VAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/msssim_vae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/msssim_vae_normal_sampling_mnist.png\" alt=\"MSSSIM_VAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/msssim_vae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/msssim_vae_normal_sampling_celeba.png\" alt=\"MSSSIM_VAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eWAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/wae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/wae_normal_sampling_mnist.png\" alt=\"WAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/wae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/wae_normal_sampling_celeba.png\" alt=\"WAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eINFO VAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/infovae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/infovae_normal_sampling_mnist.png\" alt=\"INFO Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/infovae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/infovae_normal_sampling_celeba.png\" alt=\"INFO Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eSVAE + HypershereUniformSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/svae_hypersphere_uniform_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/svae_hypersphere_uniform_sampling_mnist.png\" alt=\"SVAE Sphere\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/svae_hypersphere_uniform_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/svae_hypersphere_uniform_sampling_celeba.png\" alt=\"SVAE Sphere\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAMP + VAMPSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vamp_vamp_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vamp_vamp_sampling_mnist.png\" alt=\"VAMP Vamp\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vamp_vamp_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vamp_vamp_sampling_celeba.png\" alt=\"VAMP Vamp\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eAdversarial_AE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/aae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/aae_normal_sampling_mnist.png\" alt=\"AAE_Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/aae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/aae_normal_sampling_celeba.png\" alt=\"AAE_Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVAEGAN + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vaegan_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vaegan_normal_sampling_mnist.png\" alt=\"VAEGAN_Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vaegan_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vaegan_normal_sampling_celeba.png\" alt=\"VAEGAN_Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eVQVAE + MAFSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vqvae_maf_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vqvae_maf_sampling_mnist.png\" alt=\"VQVAE_MAF\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vqvae_maf_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vqvae_maf_sampling_celeba.png\" alt=\"VQVAE_MAF\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eHVAE + NormalSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/hvae_normal_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/hvae_normal_sampling_mnist.png\" alt=\"HVAE Normal\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/hvae_normal_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/hvae_normal_sampling_celeba.png\" alt=\"HVAE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRAE_L2 + GaussianMixtureSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_l2_gmm_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_l2_gmm_sampling_mnist.png\" alt=\"RAE L2 GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_l2_gmm_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_l2_gmm_sampling_celeba.png\" alt=\"RAE L2 GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRAE_GP + GaussianMixtureSampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_gp_gmm_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_gp_gmm_sampling_mnist.png\" alt=\"RAE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_gp_gmm_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_gp_gmm_sampling_celeba.png\" alt=\"RAE GMM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003eRiemannian Hamiltonian VAE (RHVAE) + RHVAE Sampler\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rhvae_rhvae_sampling_mnist.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rhvae_rhvae_sampling_mnist.png\" alt=\"RHVAE RHVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rhvae_rhvae_sampling_celeba.png\"\u003e\u003cimg src=\"https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rhvae_rhvae_sampling_celeba.png\" alt=\"RHVAE RHVAE\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h1\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you find this work useful or use it in your research, please consider citing us\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@inproceedings{chadebec2022pythae,\n author = {Chadebec, Cl\\'{e}ment and Vincent, Louis and Allassonniere, Stephanie},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},\n pages = {21575--21589},\n publisher = {Curran Associates, Inc.},\n title = {Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case},\n volume = {35},\n year = {2022}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@inproceedings\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003echadebec2022pythae\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eChadebec, Cl\\'{e}ment and Vincent, Louis and Allassonniere, Stephanie\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebooktitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eAdvances in Neural Information Processing Systems\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eeditor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eS. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epages\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e21575--21589\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003epublisher\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eCurran Associates, Inc.\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ePythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003evolume\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e35\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2022\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"pythae","anchor":"pythae","htmlText":"pythae"},{"level":2,"text":"Quick access:","anchor":"quick-access","htmlText":"Quick access:"},{"level":1,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Available Models","anchor":"available-models","htmlText":"Available Models"},{"level":2,"text":"Available Samplers","anchor":"available-samplers","htmlText":"Available Samplers"},{"level":2,"text":"Reproducibility","anchor":"reproducibility","htmlText":"Reproducibility"},{"level":2,"text":"Launching a model training","anchor":"launching-a-model-training","htmlText":"Launching a model training"},{"level":2,"text":"Launching a training on benchmark datasets","anchor":"launching-a-training-on-benchmark-datasets","htmlText":"Launching a training on benchmark datasets"},{"level":2,"text":"Launching data generation","anchor":"launching-data-generation","htmlText":"Launching data generation"},{"level":3,"text":"Using the GenerationPipeline","anchor":"using-the-generationpipeline","htmlText":"Using the GenerationPipeline"},{"level":3,"text":"Using the Samplers","anchor":"using-the-samplers","htmlText":"Using the Samplers"},{"level":2,"text":"Define you own Autoencoder architecture","anchor":"define-you-own-autoencoder-architecture","htmlText":"Define you own Autoencoder architecture"},{"level":2,"text":"Using benchmark neural nets","anchor":"using-benchmark-neural-nets","htmlText":"Using benchmark neural nets"},{"level":2,"text":"Distributed Training with Pythae","anchor":"distributed-training-with-pythae","htmlText":"Distributed Training with Pythae"},{"level":3,"text":"Benchmark","anchor":"benchmark","htmlText":"Benchmark"},{"level":2,"text":"Sharing your models with the HuggingFace Hub 🤗","anchor":"sharing-your-models-with-the-huggingface-hub-","htmlText":"Sharing your models with the HuggingFace Hub 🤗"},{"level":3,"text":"Uploading a model to the Hub","anchor":"uploading-a-model-to-the-hub","htmlText":"Uploading a model to the Hub"},{"level":3,"text":"Downloading models from the Hub","anchor":"downloading-models-from-the-hub","htmlText":"Downloading models from the Hub"},{"level":2,"text":"Monitoring your experiments with wandb 🧪","anchor":"monitoring-your-experiments-with-wandb-","htmlText":"Monitoring your experiments with wandb 🧪"},{"level":3,"text":"Creating a WandbCallback","anchor":"creating-a-wandbcallback","htmlText":"Creating a WandbCallback"},{"level":2,"text":"Monitoring your experiments with mlflow 🧪","anchor":"monitoring-your-experiments-with-mlflow-","htmlText":"Monitoring your experiments with mlflow 🧪"},{"level":3,"text":"Creating a MLFlowCallback","anchor":"creating-a-mlflowcallback","htmlText":"Creating a MLFlowCallback"},{"level":2,"text":"Monitoring your experiments with comet_ml 🧪","anchor":"monitoring-your-experiments-with-comet_ml-","htmlText":"Monitoring your experiments with comet_ml 🧪"},{"level":3,"text":"Creating a CometCallback","anchor":"creating-a-cometcallback","htmlText":"Creating a CometCallback"},{"level":2,"text":"Getting your hands on the code","anchor":"getting-your-hands-on-the-code","htmlText":"Getting your hands on the code"},{"level":2,"text":"Dealing with issues 🛠️","anchor":"dealing-with-issues-️","htmlText":"Dealing with issues 🛠️"},{"level":2,"text":"Contributing 🚀","anchor":"contributing-","htmlText":"Contributing 🚀"},{"level":2,"text":"Results","anchor":"results","htmlText":"Results"},{"level":3,"text":"Reconstruction","anchor":"reconstruction","htmlText":"Reconstruction"},{"level":3,"text":"Generation","anchor":"generation","htmlText":"Generation"},{"level":1,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fclementchadebec%2Fbenchmark_VAE"}},{"displayName":"LICENSE","repoName":"benchmark_VAE","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fclementchadebec%2Fbenchmark_VAE"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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<img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </p> <p dir="auto"></p> <p align="center" dir="auto"> <a href="https://pythae.readthedocs.io/en/latest/" rel="nofollow">Documentation</a> </p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">pythae</h1><a id="user-content-pythae" class="anchor" aria-label="Permalink: pythae" href="#pythae"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This library implements some of the most common (Variational) Autoencoder models under a unified implementation. In particular, it provides the possibility to perform benchmark experiments and comparisons by training the models with the same autoencoding neural network architecture. The feature <em>make your own autoencoder</em> allows you to train any of these models with your own data and own Encoder and Decoder neural networks. It integrates experiment monitoring tools such <a href="https://wandb.ai/" rel="nofollow">wandb</a>, <a href="https://mlflow.org/" rel="nofollow">mlflow</a> or <a href="https://www.comet.com/signup?utm_source=pythae&utm_medium=partner&utm_campaign=AMS_US_EN_SNUP_Pythae_Comet_Integration" rel="nofollow">comet-ml</a> 🧪 and allows model sharing and loading from the <a href="https://huggingface.co/models" rel="nofollow">HuggingFace Hub</a> 🤗 in a few lines of code.</p> <p dir="auto"><strong>News</strong> 📢</p> <p dir="auto">As of v0.1.0, <code>Pythae</code> now supports distributed training using PyTorch's <a href="https://pytorch.org/docs/stable/notes/ddp.html" rel="nofollow">DDP</a>. You can now train your favorite VAE faster and on larger datasets, still with a few lines of code. See our speed-up <a href="#benchmark">benchmark</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quick access:</h2><a id="user-content-quick-access" class="anchor" aria-label="Permalink: Quick access:" href="#quick-access"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="#installation">Installation</a></li> <li><a href="#available-models">Implemented models</a> / <a href="#available-samplers">Implemented samplers</a></li> <li><a href="#reproducibility">Reproducibility statement</a> / <a href="#results">Results flavor</a></li> <li><a href="#launching-a-model-training">Model training</a> / <a href="#launching-data-generation">Data generation</a> / <a href="#define-you-own-autoencoder-architecture">Custom network architectures</a> / <a href="#distributed-training-with-pythae">Distributed training</a></li> <li><a href="#sharing-your-models-with-the-huggingface-hub-">Model sharing with 🤗 Hub</a> / <a href="#monitoring-your-experiments-with-wandb-">Experiment tracking with <code>wandb</code></a> / <a href="#monitoring-your-experiments-with-mlflow-">Experiment tracking with <code>mlflow</code></a> / <a href="#monitoring-your-experiments-with-comet_ml-">Experiment tracking with <code>comet_ml</code></a></li> <li><a href="#getting-your-hands-on-the-code">Tutorials</a> / <a href="https://pythae.readthedocs.io/en/latest/" rel="nofollow">Documentation</a></li> <li><a href="#contributing-">Contributing 🚀</a> / <a href="#dealing-with-issues-%EF%B8%8F">Issues 🛠️</a></li> <li><a href="#citation">Citing this repository</a></li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Installation</h1><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To install the latest stable release of this library run the following using <code>pip</code></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip install pythae"><pre>$ pip install pythae</pre></div> <p dir="auto">To install the latest github version of this library run the following using <code>pip</code></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip install git+https://github.com/clementchadebec/benchmark_VAE.git"><pre>$ pip install git+https://github.com/clementchadebec/benchmark_VAE.git</pre></div> <p dir="auto">or alternatively you can clone the github repo to access to tests, tutorials and scripts.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ git clone https://github.com/clementchadebec/benchmark_VAE.git"><pre>$ git clone https://github.com/clementchadebec/benchmark_VAE.git</pre></div> <p dir="auto">and install the library</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ cd benchmark_VAE $ pip install -e ."><pre>$ <span class="pl-c1">cd</span> benchmark_VAE $ pip install -e <span class="pl-c1">.</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Available Models</h2><a id="user-content-available-models" class="anchor" aria-label="Permalink: Available Models" href="#available-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Below is the list of the models currently implemented in the library.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Models</th> <th align="center">Training example</th> <th align="center">Paper</th> <th align="center">Official Implementation</th> </tr> </thead> <tbody> <tr> <td align="center">Autoencoder (AE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/ae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"></td> <td align="center"></td> </tr> <tr> <td align="center">Variational Autoencoder (VAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1312.6114" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">Beta Variational Autoencoder (BetaVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/beta_vae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://openreview.net/pdf?id=Sy2fzU9gl" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">VAE with Linear Normalizing Flows (VAE_LinNF)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_lin_nf_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1505.05770" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">VAE with Inverse Autoregressive Flows (VAE_IAF)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_iaf_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1606.04934" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/openai/iaf">link</a></td> </tr> <tr> <td align="center">Disentangled Beta Variational Autoencoder (DisentangledBetaVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/disentangled_beta_vae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1804.03599" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">Disentangling by Factorising (FactorVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/factor_vae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1802.05983" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">Beta-TC-VAE (BetaTCVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/beta_tc_vae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1802.04942" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/rtqichen/beta-tcvae">link</a></td> </tr> <tr> <td align="center">Importance Weighted Autoencoder (IWAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/iwae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1509.00519v4" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/yburda/iwae">link</a></td> </tr> <tr> <td align="center">Multiply Importance Weighted Autoencoder (MIWAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/miwae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1802.04537" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">Partially Importance Weighted Autoencoder (PIWAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/piwae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1802.04537" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">Combination Importance Weighted Autoencoder (CIWAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/ciwae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1802.04537" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">VAE with perceptual metric similarity (MSSSIM_VAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/ms_ssim_vae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1511.06409" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">Wasserstein Autoencoder (WAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/wae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1711.01558" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/tolstikhin/wae">link</a></td> </tr> <tr> <td align="center">Info Variational Autoencoder (INFOVAE_MMD)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/info_vae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1706.02262" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">VAMP Autoencoder (VAMP)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vamp_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1705.07120" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/jmtomczak/vae_vampprior">link</a></td> </tr> <tr> <td align="center">Hyperspherical VAE (SVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/svae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1804.00891" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/nicola-decao/s-vae-pytorch">link</a></td> </tr> <tr> <td align="center">Poincaré Disk VAE (PoincareVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/pvae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1901.06033" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/emilemathieu/pvae">link</a></td> </tr> <tr> <td align="center">Adversarial Autoencoder (Adversarial_AE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/adversarial_ae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1511.05644" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">Variational Autoencoder GAN (VAEGAN) 🥗</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vaegan_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1512.09300" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/andersbll/autoencoding_beyond_pixels">link</a></td> </tr> <tr> <td align="center">Vector Quantized VAE (VQVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vqvae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1711.00937" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/deepmind/sonnet/blob/v2/sonnet/">link</a></td> </tr> <tr> <td align="center">Hamiltonian VAE (HVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/hvae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1805.11328" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/anthonycaterini/hvae-nips">link</a></td> </tr> <tr> <td align="center">Regularized AE with L2 decoder param (RAE_L2)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rae_l2_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1903.12436" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/ParthaEth/Regularized_autoencoders-RAE-/tree/master/">link</a></td> </tr> <tr> <td align="center">Regularized AE with gradient penalty (RAE_GP)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rae_gp_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/1903.12436" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/ParthaEth/Regularized_autoencoders-RAE-/tree/master/">link</a></td> </tr> <tr> <td align="center">Riemannian Hamiltonian VAE (RHVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rhvae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://arxiv.org/abs/2105.00026" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/clementchadebec/pyraug">link</a></td> </tr> <tr> <td align="center">Hierarchical Residual Quantization (HRQVAE)</td> <td align="center"><a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/hrqvae_training.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></td> <td align="center"><a href="https://aclanthology.org/2022.acl-long.178/" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/tomhosking/hrq-vae">link</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto"><strong>See <a href="#Reconstruction">reconstruction</a> and <a href="#Generation">generation</a> results for all aforementionned models</strong></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Available Samplers</h2><a id="user-content-available-samplers" class="anchor" aria-label="Permalink: Available Samplers" href="#available-samplers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Below is the list of the models currently implemented in the library.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Samplers</th> <th align="center">Models</th> <th align="center">Paper</th> <th align="center">Official Implementation</th> </tr> </thead> <tbody> <tr> <td align="center">Normal prior (NormalSampler)</td> <td align="center">all models</td> <td align="center"><a href="https://arxiv.org/abs/1312.6114" rel="nofollow">link</a></td> <td align="center"></td> </tr> <tr> <td align="center">Gaussian mixture (GaussianMixtureSampler)</td> <td align="center">all models</td> <td align="center"><a href="https://arxiv.org/abs/1903.12436" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/ParthaEth/Regularized_autoencoders-RAE-/tree/master/models/rae">link</a></td> </tr> <tr> <td align="center">Two stage VAE sampler (TwoStageVAESampler)</td> <td align="center">all VAE based models</td> <td align="center"><a href="https://openreview.net/pdf?id=B1e0X3C9tQ" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/daib13/TwoStageVAE/">link</a></td> </tr> <tr> <td align="center">Unit sphere uniform sampler (HypersphereUniformSampler)</td> <td align="center">SVAE</td> <td align="center"><a href="https://arxiv.org/abs/1804.00891" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/nicola-decao/s-vae-pytorch">link</a></td> </tr> <tr> <td align="center">Poincaré Disk sampler (PoincareDiskSampler)</td> <td align="center">PoincareVAE</td> <td align="center"><a href="https://arxiv.org/abs/1901.06033" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/emilemathieu/pvae">link</a></td> </tr> <tr> <td align="center">VAMP prior sampler (VAMPSampler)</td> <td align="center">VAMP</td> <td align="center"><a href="https://arxiv.org/abs/1705.07120" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/jmtomczak/vae_vampprior">link</a></td> </tr> <tr> <td align="center">Manifold sampler (RHVAESampler)</td> <td align="center">RHVAE</td> <td align="center"><a href="https://arxiv.org/abs/2105.00026" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/clementchadebec/pyraug">link</a></td> </tr> <tr> <td align="center">Masked Autoregressive Flow Sampler (MAFSampler)</td> <td align="center">all models</td> <td align="center"><a href="https://arxiv.org/abs/1705.07057v4" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/gpapamak/maf">link</a></td> </tr> <tr> <td align="center">Inverse Autoregressive Flow Sampler (IAFSampler)</td> <td align="center">all models</td> <td align="center"><a href="https://arxiv.org/abs/1606.04934" rel="nofollow">link</a></td> <td align="center"><a href="https://github.com/openai/iaf">link</a></td> </tr> <tr> <td align="center">PixelCNN (PixelCNNSampler)</td> <td align="center">VQVAE</td> <td align="center"><a href="https://arxiv.org/abs/1606.05328" rel="nofollow">link</a></td> <td align="center"></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Reproducibility</h2><a id="user-content-reproducibility" class="anchor" aria-label="Permalink: Reproducibility" href="#reproducibility"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We validate the implementations by reproducing some results presented in the original publications when the official code has been released or when enough details about the experimental section of the papers were available. See <a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts/reproducibility">reproducibility</a> for more details.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Launching a model training</h2><a id="user-content-launching-a-model-training" class="anchor" aria-label="Permalink: Launching a model training" href="#launching-a-model-training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To launch a model training, you only need to call a <code>TrainingPipeline</code> instance.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> from pythae.pipelines import TrainingPipeline >>> from pythae.models import VAE, VAEConfig >>> from pythae.trainers import BaseTrainerConfig >>> # Set up the training configuration >>> my_training_config = BaseTrainerConfig( ... output_dir='my_model', ... num_epochs=50, ... learning_rate=1e-3, ... per_device_train_batch_size=200, ... per_device_eval_batch_size=200, ... train_dataloader_num_workers=2, ... eval_dataloader_num_workers=2, ... steps_saving=20, ... optimizer_cls="AdamW", ... optimizer_params={"weight_decay": 0.05, "betas": (0.91, 0.995)}, ... scheduler_cls="ReduceLROnPlateau", ... scheduler_params={"patience": 5, "factor": 0.5} ... ) >>> # Set up the model configuration >>> my_vae_config = model_config = VAEConfig( ... input_dim=(1, 28, 28), ... latent_dim=10 ... ) >>> # Build the model >>> my_vae_model = VAE( ... model_config=my_vae_config ... ) >>> # Build the Pipeline >>> pipeline = TrainingPipeline( ... training_config=my_training_config, ... model=my_vae_model ... ) >>> # Launch the Pipeline >>> pipeline( ... train_data=your_train_data, # must be torch.Tensor, np.array or torch datasets ... eval_data=your_eval_data # must be torch.Tensor, np.array or torch datasets ... )"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">pipelines</span> <span class="pl-k">import</span> <span class="pl-v">TrainingPipeline</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span> <span class="pl-k">import</span> <span class="pl-v">VAE</span>, <span class="pl-v">VAEConfig</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">trainers</span> <span class="pl-k">import</span> <span class="pl-v">BaseTrainerConfig</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Set up the training configuration</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_training_config</span> <span class="pl-c1">=</span> <span class="pl-v">BaseTrainerConfig</span>( ... <span class="pl-s1">output_dir</span><span class="pl-c1">=</span><span class="pl-s">'my_model'</span>, ... <span class="pl-s1">num_epochs</span><span class="pl-c1">=</span><span class="pl-c1">50</span>, ... <span class="pl-s1">learning_rate</span><span class="pl-c1">=</span><span class="pl-c1">1e-3</span>, ... <span class="pl-s1">per_device_train_batch_size</span><span class="pl-c1">=</span><span class="pl-c1">200</span>, ... <span class="pl-s1">per_device_eval_batch_size</span><span class="pl-c1">=</span><span class="pl-c1">200</span>, ... <span class="pl-s1">train_dataloader_num_workers</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, ... <span class="pl-s1">eval_dataloader_num_workers</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, ... <span class="pl-s1">steps_saving</span><span class="pl-c1">=</span><span class="pl-c1">20</span>, ... <span class="pl-s1">optimizer_cls</span><span class="pl-c1">=</span><span class="pl-s">"AdamW"</span>, ... <span class="pl-s1">optimizer_params</span><span class="pl-c1">=</span>{<span class="pl-s">"weight_decay"</span>: <span class="pl-c1">0.05</span>, <span class="pl-s">"betas"</span>: (<span class="pl-c1">0.91</span>, <span class="pl-c1">0.995</span>)}, ... <span class="pl-s1">scheduler_cls</span><span class="pl-c1">=</span><span class="pl-s">"ReduceLROnPlateau"</span>, ... <span class="pl-s1">scheduler_params</span><span class="pl-c1">=</span>{<span class="pl-s">"patience"</span>: <span class="pl-c1">5</span>, <span class="pl-s">"factor"</span>: <span class="pl-c1">0.5</span>} ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Set up the model configuration </span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_vae_config</span> <span class="pl-c1">=</span> <span class="pl-s1">model_config</span> <span class="pl-c1">=</span> <span class="pl-v">VAEConfig</span>( ... <span class="pl-s1">input_dim</span><span class="pl-c1">=</span>(<span class="pl-c1">1</span>, <span class="pl-c1">28</span>, <span class="pl-c1">28</span>), ... <span class="pl-s1">latent_dim</span><span class="pl-c1">=</span><span class="pl-c1">10</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Build the model</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_vae_model</span> <span class="pl-c1">=</span> <span class="pl-v">VAE</span>( ... <span class="pl-s1">model_config</span><span class="pl-c1">=</span><span class="pl-s1">my_vae_config</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Build the Pipeline</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">pipeline</span> <span class="pl-c1">=</span> <span class="pl-v">TrainingPipeline</span>( ... <span class="pl-s1">training_config</span><span class="pl-c1">=</span><span class="pl-s1">my_training_config</span>, ... <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">my_vae_model</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Launch the Pipeline</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-en">pipeline</span>( ... <span class="pl-s1">train_data</span><span class="pl-c1">=</span><span class="pl-s1">your_train_data</span>, <span class="pl-c"># must be torch.Tensor, np.array or torch datasets</span> ... <span class="pl-s1">eval_data</span><span class="pl-c1">=</span><span class="pl-s1">your_eval_data</span> <span class="pl-c"># must be torch.Tensor, np.array or torch datasets</span> ... )</pre></div> <p dir="auto">At the end of training, the best model weights, model configuration and training configuration are stored in a <code>final_model</code> folder available in <code>my_model/MODEL_NAME_training_YYYY-MM-DD_hh-mm-ss</code> (with <code>my_model</code> being the <code>output_dir</code> argument of the <code>BaseTrainerConfig</code>). If you further set the <code>steps_saving</code> argument to a certain value, folders named <code>checkpoint_epoch_k</code> containing the best model weights, optimizer, scheduler, configuration and training configuration at epoch <em>k</em> will also appear in <code>my_model/MODEL_NAME_training_YYYY-MM-DD_hh-mm-ss</code>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Launching a training on benchmark datasets</h2><a id="user-content-launching-a-training-on-benchmark-datasets" class="anchor" aria-label="Permalink: Launching a training on benchmark datasets" href="#launching-a-training-on-benchmark-datasets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We also provide a training script example <a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts/training.py">here</a> that can be used to train the models on benchmarks datasets (mnist, cifar10, celeba ...). The script can be launched with the following commandline</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python training.py --dataset mnist --model_name ae --model_config 'configs/ae_config.json' --training_config 'configs/base_training_config.json'"><pre>python training.py --dataset mnist --model_name ae --model_config <span class="pl-s"><span class="pl-pds">'</span>configs/ae_config.json<span class="pl-pds">'</span></span> --training_config <span class="pl-s"><span class="pl-pds">'</span>configs/base_training_config.json<span class="pl-pds">'</span></span></pre></div> <p dir="auto">See <a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts/README.md">README.md</a> for further details on this script</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Launching data generation</h2><a id="user-content-launching-data-generation" class="anchor" aria-label="Permalink: Launching data generation" href="#launching-data-generation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Using the <code>GenerationPipeline</code></h3><a id="user-content-using-the-generationpipeline" class="anchor" aria-label="Permalink: Using the GenerationPipeline" href="#using-the-generationpipeline"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The easiest way to launch a data generation from a trained model consists in using the built-in <code>GenerationPipeline</code> provided in Pythae. Say you want to generate 100 samples using a <code>MAFSampler</code> all you have to do is 1) relaod the trained model, 2) define the sampler's configuration and 3) create and launch the <code>GenerationPipeline</code> as follows</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> from pythae.models import AutoModel >>> from pythae.samplers import MAFSamplerConfig >>> from pythae.pipelines import GenerationPipeline >>> # Retrieve the trained model >>> my_trained_vae = AutoModel.load_from_folder( ... 'path/to/your/trained/model' ... ) >>> my_sampler_config = MAFSamplerConfig( ... n_made_blocks=2, ... n_hidden_in_made=3, ... hidden_size=128 ... ) >>> # Build the pipeline >>> pipe = GenerationPipeline( ... model=my_trained_vae, ... sampler_config=my_sampler_config ... ) >>> # Launch data generation >>> generated_samples = pipe( ... num_samples=args.num_samples, ... return_gen=True, # If false returns nothing ... train_data=train_data, # Needed to fit the sampler ... eval_data=eval_data, # Needed to fit the sampler ... training_config=BaseTrainerConfig(num_epochs=200) # TrainingConfig to use to fit the sampler ... )"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span> <span class="pl-k">import</span> <span class="pl-v">AutoModel</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">samplers</span> <span class="pl-k">import</span> <span class="pl-v">MAFSamplerConfig</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">pipelines</span> <span class="pl-k">import</span> <span class="pl-v">GenerationPipeline</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Retrieve the trained model</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_trained_vae</span> <span class="pl-c1">=</span> <span class="pl-v">AutoModel</span>.<span class="pl-en">load_from_folder</span>( ... <span class="pl-s">'path/to/your/trained/model'</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_sampler_config</span> <span class="pl-c1">=</span> <span class="pl-v">MAFSamplerConfig</span>( ... <span class="pl-s1">n_made_blocks</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, ... <span class="pl-s1">n_hidden_in_made</span><span class="pl-c1">=</span><span class="pl-c1">3</span>, ... <span class="pl-s1">hidden_size</span><span class="pl-c1">=</span><span class="pl-c1">128</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Build the pipeline</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">pipe</span> <span class="pl-c1">=</span> <span class="pl-v">GenerationPipeline</span>( ... <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">my_trained_vae</span>, ... <span class="pl-s1">sampler_config</span><span class="pl-c1">=</span><span class="pl-s1">my_sampler_config</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Launch data generation</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">generated_samples</span> <span class="pl-c1">=</span> <span class="pl-en">pipe</span>( ... <span class="pl-s1">num_samples</span><span class="pl-c1">=</span><span class="pl-s1">args</span>.<span class="pl-s1">num_samples</span>, ... <span class="pl-s1">return_gen</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-c"># If false returns nothing</span> ... <span class="pl-s1">train_data</span><span class="pl-c1">=</span><span class="pl-s1">train_data</span>, <span class="pl-c"># Needed to fit the sampler</span> ... <span class="pl-s1">eval_data</span><span class="pl-c1">=</span><span class="pl-s1">eval_data</span>, <span class="pl-c"># Needed to fit the sampler</span> ... <span class="pl-s1">training_config</span><span class="pl-c1">=</span><span class="pl-v">BaseTrainerConfig</span>(<span class="pl-s1">num_epochs</span><span class="pl-c1">=</span><span class="pl-c1">200</span>) <span class="pl-c"># TrainingConfig to use to fit the sampler</span> ... )</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Using the Samplers</h3><a id="user-content-using-the-samplers" class="anchor" aria-label="Permalink: Using the Samplers" href="#using-the-samplers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Alternatively, you can launch the data generation process from a trained model directly with the sampler. For instance, to generate new data with your sampler, run the following.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> from pythae.models import AutoModel >>> from pythae.samplers import NormalSampler >>> # Retrieve the trained model >>> my_trained_vae = AutoModel.load_from_folder( ... 'path/to/your/trained/model' ... ) >>> # Define your sampler >>> my_samper = NormalSampler( ... model=my_trained_vae ... ) >>> # Generate samples >>> gen_data = my_samper.sample( ... num_samples=50, ... batch_size=10, ... output_dir=None, ... return_gen=True ... )"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span> <span class="pl-k">import</span> <span class="pl-v">AutoModel</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">samplers</span> <span class="pl-k">import</span> <span class="pl-v">NormalSampler</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Retrieve the trained model</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_trained_vae</span> <span class="pl-c1">=</span> <span class="pl-v">AutoModel</span>.<span class="pl-en">load_from_folder</span>( ... <span class="pl-s">'path/to/your/trained/model'</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Define your sampler</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_samper</span> <span class="pl-c1">=</span> <span class="pl-v">NormalSampler</span>( ... <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">my_trained_vae</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Generate samples</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">gen_data</span> <span class="pl-c1">=</span> <span class="pl-s1">my_samper</span>.<span class="pl-en">sample</span>( ... <span class="pl-s1">num_samples</span><span class="pl-c1">=</span><span class="pl-c1">50</span>, ... <span class="pl-s1">batch_size</span><span class="pl-c1">=</span><span class="pl-c1">10</span>, ... <span class="pl-s1">output_dir</span><span class="pl-c1">=</span><span class="pl-c1">None</span>, ... <span class="pl-s1">return_gen</span><span class="pl-c1">=</span><span class="pl-c1">True</span> ... )</pre></div> <p dir="auto">If you set <code>output_dir</code> to a specific path, the generated images will be saved as <code>.png</code> files named <code>00000000.png</code>, <code>00000001.png</code> ... The samplers can be used with any model as long as it is suited. For instance, a <code>GaussianMixtureSampler</code> instance can be used to generate from any model but a <code>VAMPSampler</code> will only be usable with a <code>VAMP</code> model. Check <a href="#available-samplers">here</a> to see which ones apply to your model. Be carefull that some samplers such as the <code>GaussianMixtureSampler</code> for instance may need to be fitted by calling the <code>fit</code> method before using. Below is an example for the <code>GaussianMixtureSampler</code>.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> from pythae.models import AutoModel >>> from pythae.samplers import GaussianMixtureSampler, GaussianMixtureSamplerConfig >>> # Retrieve the trained model >>> my_trained_vae = AutoModel.load_from_folder( ... 'path/to/your/trained/model' ... ) >>> # Define your sampler ... gmm_sampler_config = GaussianMixtureSamplerConfig( ... n_components=10 ... ) >>> my_samper = GaussianMixtureSampler( ... sampler_config=gmm_sampler_config, ... model=my_trained_vae ... ) >>> # fit the sampler >>> gmm_sampler.fit(train_dataset) >>> # Generate samples >>> gen_data = my_samper.sample( ... num_samples=50, ... batch_size=10, ... output_dir=None, ... return_gen=True ... )"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span> <span class="pl-k">import</span> <span class="pl-v">AutoModel</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">samplers</span> <span class="pl-k">import</span> <span class="pl-v">GaussianMixtureSampler</span>, <span class="pl-v">GaussianMixtureSamplerConfig</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Retrieve the trained model</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_trained_vae</span> <span class="pl-c1">=</span> <span class="pl-v">AutoModel</span>.<span class="pl-en">load_from_folder</span>( ... <span class="pl-s">'path/to/your/trained/model'</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Define your sampler</span> ... <span class="pl-s1">gmm_sampler_config</span> <span class="pl-c1">=</span> <span class="pl-v">GaussianMixtureSamplerConfig</span>( ... <span class="pl-s1">n_components</span><span class="pl-c1">=</span><span class="pl-c1">10</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_samper</span> <span class="pl-c1">=</span> <span class="pl-v">GaussianMixtureSampler</span>( ... <span class="pl-s1">sampler_config</span><span class="pl-c1">=</span><span class="pl-s1">gmm_sampler_config</span>, ... <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">my_trained_vae</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># fit the sampler</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">gmm_sampler</span>.<span class="pl-en">fit</span>(<span class="pl-s1">train_dataset</span>) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Generate samples</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">gen_data</span> <span class="pl-c1">=</span> <span class="pl-s1">my_samper</span>.<span class="pl-en">sample</span>( ... <span class="pl-s1">num_samples</span><span class="pl-c1">=</span><span class="pl-c1">50</span>, ... <span class="pl-s1">batch_size</span><span class="pl-c1">=</span><span class="pl-c1">10</span>, ... <span class="pl-s1">output_dir</span><span class="pl-c1">=</span><span class="pl-c1">None</span>, ... <span class="pl-s1">return_gen</span><span class="pl-c1">=</span><span class="pl-c1">True</span> ... )</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Define you own Autoencoder architecture</h2><a id="user-content-define-you-own-autoencoder-architecture" class="anchor" aria-label="Permalink: Define you own Autoencoder architecture" href="#define-you-own-autoencoder-architecture"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pythae provides you the possibility to define your own neural networks within the VAE models. For instance, say you want to train a Wassertstein AE with a specific encoder and decoder, you can do the following:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> from pythae.models.nn import BaseEncoder, BaseDecoder >>> from pythae.models.base.base_utils import ModelOutput >>> class My_Encoder(BaseEncoder): ... def __init__(self, args=None): # Args is a ModelConfig instance ... BaseEncoder.__init__(self) ... self.layers = my_nn_layers() ... ... def forward(self, x:torch.Tensor) -> ModelOutput: ... out = self.layers(x) ... output = ModelOutput( ... embedding=out # Set the output from the encoder in a ModelOutput instance ... ) ... return output ... ... class My_Decoder(BaseDecoder): ... def __init__(self, args=None): ... BaseDecoder.__init__(self) ... self.layers = my_nn_layers() ... ... def forward(self, x:torch.Tensor) -> ModelOutput: ... out = self.layers(x) ... output = ModelOutput( ... reconstruction=out # Set the output from the decoder in a ModelOutput instance ... ) ... return output ... >>> my_encoder = My_Encoder() >>> my_decoder = My_Decoder()"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span>.<span class="pl-s1">nn</span> <span class="pl-k">import</span> <span class="pl-v">BaseEncoder</span>, <span class="pl-v">BaseDecoder</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span>.<span class="pl-s1">base</span>.<span class="pl-s1">base_utils</span> <span class="pl-k">import</span> <span class="pl-v">ModelOutput</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">class</span> <span class="pl-v">My_Encoder</span>(<span class="pl-v">BaseEncoder</span>): ... <span class="pl-k">def</span> <span class="pl-en">__init__</span>(<span class="pl-s1">self</span>, <span class="pl-s1">args</span><span class="pl-c1">=</span><span class="pl-c1">None</span>): <span class="pl-c"># Args is a ModelConfig instance</span> ... <span class="pl-v">BaseEncoder</span>.<span class="pl-en">__init__</span>(<span class="pl-s1">self</span>) ... <span class="pl-s1">self</span>.<span class="pl-s1">layers</span> <span class="pl-c1">=</span> <span class="pl-en">my_nn_layers</span>() ... ... <span class="pl-k">def</span> <span class="pl-en">forward</span>(<span class="pl-s1">self</span>, <span class="pl-s1">x</span>:<span class="pl-s1">torch</span>.<span class="pl-v">Tensor</span>) <span class="pl-c1">-></span> <span class="pl-v">ModelOutput</span>: ... <span class="pl-s1">out</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-en">layers</span>(<span class="pl-s1">x</span>) ... <span class="pl-s1">output</span> <span class="pl-c1">=</span> <span class="pl-v">ModelOutput</span>( ... <span class="pl-s1">embedding</span><span class="pl-c1">=</span><span class="pl-s1">out</span> <span class="pl-c"># Set the output from the encoder in a ModelOutput instance </span> ... ) ... <span class="pl-k">return</span> <span class="pl-s1">output</span> ... ... <span class="pl-k">class</span> <span class="pl-v">My_Decoder</span>(<span class="pl-v">BaseDecoder</span>): ... <span class="pl-k">def</span> <span class="pl-en">__init__</span>(<span class="pl-s1">self</span>, <span class="pl-s1">args</span><span class="pl-c1">=</span><span class="pl-c1">None</span>): ... <span class="pl-v">BaseDecoder</span>.<span class="pl-en">__init__</span>(<span class="pl-s1">self</span>) ... <span class="pl-s1">self</span>.<span class="pl-s1">layers</span> <span class="pl-c1">=</span> <span class="pl-en">my_nn_layers</span>() ... ... <span class="pl-k">def</span> <span class="pl-en">forward</span>(<span class="pl-s1">self</span>, <span class="pl-s1">x</span>:<span class="pl-s1">torch</span>.<span class="pl-v">Tensor</span>) <span class="pl-c1">-></span> <span class="pl-v">ModelOutput</span>: ... <span class="pl-s1">out</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-en">layers</span>(<span class="pl-s1">x</span>) ... <span class="pl-s1">output</span> <span class="pl-c1">=</span> <span class="pl-v">ModelOutput</span>( ... <span class="pl-s1">reconstruction</span><span class="pl-c1">=</span><span class="pl-s1">out</span> <span class="pl-c"># Set the output from the decoder in a ModelOutput instance</span> ... ) ... <span class="pl-k">return</span> <span class="pl-s1">output</span> ... <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_encoder</span> <span class="pl-c1">=</span> <span class="pl-v">My_Encoder</span>() <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_decoder</span> <span class="pl-c1">=</span> <span class="pl-v">My_Decoder</span>()</pre></div> <p dir="auto">And now build the model</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> from pythae.models import WAE_MMD, WAE_MMD_Config >>> # Set up the model configuration >>> my_wae_config = model_config = WAE_MMD_Config( ... input_dim=(1, 28, 28), ... latent_dim=10 ... ) ... >>> # Build the model >>> my_wae_model = WAE_MMD( ... model_config=my_wae_config, ... encoder=my_encoder, # pass your encoder as argument when building the model ... decoder=my_decoder # pass your decoder as argument when building the model ... )"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span> <span class="pl-k">import</span> <span class="pl-v">WAE_MMD</span>, <span class="pl-v">WAE_MMD_Config</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Set up the model configuration </span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_wae_config</span> <span class="pl-c1">=</span> <span class="pl-s1">model_config</span> <span class="pl-c1">=</span> <span class="pl-v">WAE_MMD_Config</span>( ... <span class="pl-s1">input_dim</span><span class="pl-c1">=</span>(<span class="pl-c1">1</span>, <span class="pl-c1">28</span>, <span class="pl-c1">28</span>), ... <span class="pl-s1">latent_dim</span><span class="pl-c1">=</span><span class="pl-c1">10</span> ... ) ... <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Build the model</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_wae_model</span> <span class="pl-c1">=</span> <span class="pl-v">WAE_MMD</span>( ... <span class="pl-s1">model_config</span><span class="pl-c1">=</span><span class="pl-s1">my_wae_config</span>, ... <span class="pl-s1">encoder</span><span class="pl-c1">=</span><span class="pl-s1">my_encoder</span>, <span class="pl-c"># pass your encoder as argument when building the model</span> ... <span class="pl-s1">decoder</span><span class="pl-c1">=</span><span class="pl-s1">my_decoder</span> <span class="pl-c"># pass your decoder as argument when building the model</span> ... )</pre></div> <p dir="auto"><strong>important note 1</strong>: For all AE-based models (AE, WAE, RAE_L2, RAE_GP), both the encoder and decoder must return a <code>ModelOutput</code> instance. For the encoder, the <code>ModelOutput</code> instance must contain the embbeddings under the key <code>embedding</code>. For the decoder, the <code>ModelOutput</code> instance must contain the reconstructions under the key <code>reconstruction</code>.</p> <p dir="auto"><strong>important note 2</strong>: For all VAE-based models (VAE, BetaVAE, IWAE, HVAE, VAMP, RHVAE), both the encoder and decoder must return a <code>ModelOutput</code> instance. For the encoder, the <code>ModelOutput</code> instance must contain the embbeddings and <strong>log</strong>-covariance matrices (of shape batch_size x latent_space_dim) respectively under the key <code>embedding</code> and <code>log_covariance</code> key. For the decoder, the <code>ModelOutput</code> instance must contain the reconstructions under the key <code>reconstruction</code>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Using benchmark neural nets</h2><a id="user-content-using-benchmark-neural-nets" class="anchor" aria-label="Permalink: Using benchmark neural nets" href="#using-benchmark-neural-nets"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can also find predefined neural network architectures for the most common data sets (<em>i.e.</em> MNIST, CIFAR, CELEBA ...) that can be loaded as follows</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> from pythae.models.nn.benchmark.mnist import ( ... Encoder_Conv_AE_MNIST, # For AE based model (only return embeddings) ... Encoder_Conv_VAE_MNIST, # For VAE based model (return embeddings and log_covariances) ... Decoder_Conv_AE_MNIST ... )"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span>.<span class="pl-s1">nn</span>.<span class="pl-s1">benchmark</span>.<span class="pl-s1">mnist</span> <span class="pl-k">import</span> ( ... <span class="pl-v">Encoder_Conv_AE_MNIST</span>, <span class="pl-c"># For AE based model (only return embeddings)</span> ... <span class="pl-v">Encoder_Conv_VAE_MNIST</span>, <span class="pl-c"># For VAE based model (return embeddings and log_covariances)</span> ... <span class="pl-v">Decoder_Conv_AE_MNIST</span> ... )</pre></div> <p dir="auto">Replace <em>mnist</em> by cifar or celeba to access to other neural nets.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Distributed Training with <code>Pythae</code></h2><a id="user-content-distributed-training-with-pythae" class="anchor" aria-label="Permalink: Distributed Training with Pythae" href="#distributed-training-with-pythae"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">As of <code>v0.1.0</code>, Pythae now supports distributed training using PyTorch's <a href="https://pytorch.org/docs/stable/notes/ddp.html" rel="nofollow">DDP</a>. It allows you to train your favorite VAE faster and on larger dataset using multi-gpu and/or multi-node training.</p> <p dir="auto">To do so, you can build a python script that will then be launched by a launcher (such as <code>srun</code> on a cluster). The only thing that is needed in the script is to specify some elements relative to the distributed environment (such as the number of nodes/gpus) directly in the training configuration as follows</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> training_config = BaseTrainerConfig( ... num_epochs=10, ... learning_rate=1e-3, ... per_device_train_batch_size=64, ... per_device_eval_batch_size=64, ... train_dataloader_num_workers=8, ... eval_dataloader_num_workers=8, ... dist_backend="nccl", # distributed backend ... world_size=8 # number of gpus to use (n_nodes x n_gpus_per_node), ... rank=5 # global gpu id, ... local_rank=1 # gpu id within a node, ... master_addr="localhost" # master address, ... master_port="12345" # master port, ... )"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">training_config</span> <span class="pl-c1">=</span> <span class="pl-v">BaseTrainerConfig</span>( ... <span class="pl-s1">num_epochs</span><span class="pl-c1">=</span><span class="pl-c1">10</span>, ... <span class="pl-s1">learning_rate</span><span class="pl-c1">=</span><span class="pl-c1">1e-3</span>, ... <span class="pl-s1">per_device_train_batch_size</span><span class="pl-c1">=</span><span class="pl-c1">64</span>, ... <span class="pl-s1">per_device_eval_batch_size</span><span class="pl-c1">=</span><span class="pl-c1">64</span>, ... <span class="pl-s1">train_dataloader_num_workers</span><span class="pl-c1">=</span><span class="pl-c1">8</span>, ... <span class="pl-s1">eval_dataloader_num_workers</span><span class="pl-c1">=</span><span class="pl-c1">8</span>, ... <span class="pl-s1">dist_backend</span><span class="pl-c1">=</span><span class="pl-s">"nccl"</span>, <span class="pl-c"># distributed backend</span> ... <span class="pl-s1">world_size</span><span class="pl-c1">=</span><span class="pl-c1">8</span> <span class="pl-c"># number of gpus to use (n_nodes x n_gpus_per_node),</span> ... <span class="pl-s1">rank</span><span class="pl-c1">=</span><span class="pl-c1">5</span> <span class="pl-c"># global gpu id,</span> ... <span class="pl-s1">local_rank</span><span class="pl-c1">=</span><span class="pl-c1">1</span> <span class="pl-c"># gpu id within a node,</span> ... <span class="pl-s1">master_addr</span><span class="pl-c1">=</span><span class="pl-s">"localhost"</span> <span class="pl-c"># master address,</span> ... <span class="pl-s1">master_port</span><span class="pl-c1">=</span><span class="pl-s">"12345"</span> <span class="pl-c"># master port,</span> ... )</pre></div> <p dir="auto">See this <a href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/scripts/distributed_training_imagenet.py">example script</a> that defines a multi-gpu VQVAE training on ImageNet dataset. Please note that the way the distributed environnement variables (<code>world_size</code>, <code>rank</code> ...) are recovered may be specific to the cluster and launcher you use.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Benchmark</h3><a id="user-content-benchmark" class="anchor" aria-label="Permalink: Benchmark" href="#benchmark"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Below are indicated the training times for a Vector Quantized VAE (VQ-VAE) with <code>Pythae</code> for 100 epochs on MNIST on V100 16GB GPU(s), for 50 epochs on <a href="https://github.com/NVlabs/ffhq-dataset">FFHQ</a> (1024x1024 images) and for 20 epochs on <a href="https://huggingface.co/datasets/imagenet-1k" rel="nofollow">ImageNet-1k</a> on V100 32GB GPU(s).</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center"></th> <th align="center">Train Data</th> <th align="center">1 GPU</th> <th align="center">4 GPUs</th> <th>2x4 GPUs</th> </tr> </thead> <tbody> <tr> <td align="center">MNIST (VQ-VAE)</td> <td align="center">28x28 images (50k)</td> <td align="center">235.18 s</td> <td align="center">62.00 s</td> <td>35.86 s</td> </tr> <tr> <td align="center">FFHQ 1024x1024 (VQVAE)</td> <td align="center">1024x1024 RGB images (60k)</td> <td align="center">19h 1min</td> <td align="center">5h 6min</td> <td>2h 37min</td> </tr> <tr> <td align="center">ImageNet-1k 128x128 (VQVAE)</td> <td align="center">128x128 RGB images (~ 1.2M)</td> <td align="center">6h 25min</td> <td align="center">1h 41min</td> <td>51min 26s</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">For each dataset, we provide the benchmarking scripts <a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts">here</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Sharing your models with the HuggingFace Hub 🤗</h2><a id="user-content-sharing-your-models-with-the-huggingface-hub-" class="anchor" aria-label="Permalink: Sharing your models with the HuggingFace Hub 🤗" href="#sharing-your-models-with-the-huggingface-hub-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pythae also allows you to share your models on the <a href="https://huggingface.co/models" rel="nofollow">HuggingFace Hub</a>. To do so you need:</p> <ul dir="auto"> <li>a valid HuggingFace account</li> <li>the package <code>huggingface_hub</code> installed in your virtual env. If not you can install it with</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ python -m pip install huggingface_hub"><pre class="notranslate"><code>$ python -m pip install huggingface_hub </code></pre></div> <ul dir="auto"> <li>to be logged in to your HuggingFace account using</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ huggingface-cli login"><pre class="notranslate"><code>$ huggingface-cli login </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Uploading a model to the Hub</h3><a id="user-content-uploading-a-model-to-the-hub" class="anchor" aria-label="Permalink: Uploading a model to the Hub" href="#uploading-a-model-to-the-hub"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Any pythae model can be easily uploaded using the method <code>push_to_hf_hub</code></p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> my_vae_model.push_to_hf_hub(hf_hub_path="your_hf_username/your_hf_hub_repo")"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_vae_model</span>.<span class="pl-en">push_to_hf_hub</span>(<span class="pl-s1">hf_hub_path</span><span class="pl-c1">=</span><span class="pl-s">"your_hf_username/your_hf_hub_repo"</span>)</pre></div> <p dir="auto"><strong>Note:</strong> If <code>your_hf_hub_repo</code> already exists and is not empty, files will be overridden. In case, the repo <code>your_hf_hub_repo</code> does not exist, a folder having the same name will be created.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Downloading models from the Hub</h3><a id="user-content-downloading-models-from-the-hub" class="anchor" aria-label="Permalink: Downloading models from the Hub" href="#downloading-models-from-the-hub"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Equivalently, you can download or reload any Pythae's model directly from the Hub using the method <code>load_from_hf_hub</code></p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> from pythae.models import AutoModel >>> my_downloaded_vae = AutoModel.load_from_hf_hub(hf_hub_path="path_to_hf_repo")"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">models</span> <span class="pl-k">import</span> <span class="pl-v">AutoModel</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">my_downloaded_vae</span> <span class="pl-c1">=</span> <span class="pl-v">AutoModel</span>.<span class="pl-en">load_from_hf_hub</span>(<span class="pl-s1">hf_hub_path</span><span class="pl-c1">=</span><span class="pl-s">"path_to_hf_repo"</span>)</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Monitoring your experiments with <code>wandb</code> 🧪</h2><a id="user-content-monitoring-your-experiments-with-wandb-" class="anchor" aria-label="Permalink: Monitoring your experiments with wandb 🧪" href="#monitoring-your-experiments-with-wandb-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pythae also integrates the experiment tracking tool <a href="https://wandb.ai/" rel="nofollow">wandb</a> allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:</p> <ul dir="auto"> <li>a valid wandb account</li> <li>the package <code>wandb</code> installed in your virtual env. If not you can install it with</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ pip install wandb"><pre class="notranslate"><code>$ pip install wandb </code></pre></div> <ul dir="auto"> <li>to be logged in to your wandb account using</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ wandb login"><pre class="notranslate"><code>$ wandb login </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Creating a <code>WandbCallback</code></h3><a id="user-content-creating-a-wandbcallback" class="anchor" aria-label="Permalink: Creating a WandbCallback" href="#creating-a-wandbcallback"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Launching an experiment monitoring with <code>wandb</code> in pythae is pretty simple. The only thing a user needs to do is create a <code>WandbCallback</code> instance...</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> # Create you callback >>> from pythae.trainers.training_callbacks import WandbCallback >>> callbacks = [] # the TrainingPipeline expects a list of callbacks >>> wandb_cb = WandbCallback() # Build the callback >>> # SetUp the callback >>> wandb_cb.setup( ... training_config=your_training_config, # training config ... model_config=your_model_config, # model config ... project_name="your_wandb_project", # specify your wandb project ... entity_name="your_wandb_entity", # specify your wandb entity ... ) >>> callbacks.append(wandb_cb) # Add it to the callbacks list"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Create you callback</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">trainers</span>.<span class="pl-s1">training_callbacks</span> <span class="pl-k">import</span> <span class="pl-v">WandbCallback</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">callbacks</span> <span class="pl-c1">=</span> [] <span class="pl-c"># the TrainingPipeline expects a list of callbacks</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">wandb_cb</span> <span class="pl-c1">=</span> <span class="pl-v">WandbCallback</span>() <span class="pl-c"># Build the callback </span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># SetUp the callback </span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">wandb_cb</span>.<span class="pl-en">setup</span>( ... <span class="pl-s1">training_config</span><span class="pl-c1">=</span><span class="pl-s1">your_training_config</span>, <span class="pl-c"># training config</span> ... <span class="pl-s1">model_config</span><span class="pl-c1">=</span><span class="pl-s1">your_model_config</span>, <span class="pl-c"># model config</span> ... <span class="pl-s1">project_name</span><span class="pl-c1">=</span><span class="pl-s">"your_wandb_project"</span>, <span class="pl-c"># specify your wandb project</span> ... <span class="pl-s1">entity_name</span><span class="pl-c1">=</span><span class="pl-s">"your_wandb_entity"</span>, <span class="pl-c"># specify your wandb entity</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">callbacks</span>.<span class="pl-en">append</span>(<span class="pl-s1">wandb_cb</span>) <span class="pl-c"># Add it to the callbacks list</span></pre></div> <p dir="auto">...and then pass it to the <code>TrainingPipeline</code>.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> pipeline = TrainingPipeline( ... training_config=config, ... model=model ... ) >>> pipeline( ... train_data=train_dataset, ... eval_data=eval_dataset, ... callbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done! ... ) >>> # You can log to https://wandb.ai/your_wandb_entity/your_wandb_project to monitor your training"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">pipeline</span> <span class="pl-c1">=</span> <span class="pl-v">TrainingPipeline</span>( ... <span class="pl-s1">training_config</span><span class="pl-c1">=</span><span class="pl-s1">config</span>, ... <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">model</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-en">pipeline</span>( ... <span class="pl-s1">train_data</span><span class="pl-c1">=</span><span class="pl-s1">train_dataset</span>, ... <span class="pl-s1">eval_data</span><span class="pl-c1">=</span><span class="pl-s1">eval_dataset</span>, ... <span class="pl-s1">callbacks</span><span class="pl-c1">=</span><span class="pl-s1">callbacks</span> <span class="pl-c"># pass the callbacks to the TrainingPipeline and you are done!</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># You can log to https://wandb.ai/your_wandb_entity/your_wandb_project to monitor your training</span></pre></div> <p dir="auto">See the detailed tutorial</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Monitoring your experiments with <code>mlflow</code> 🧪</h2><a id="user-content-monitoring-your-experiments-with-mlflow-" class="anchor" aria-label="Permalink: Monitoring your experiments with mlflow 🧪" href="#monitoring-your-experiments-with-mlflow-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pythae also integrates the experiment tracking tool <a href="https://mlflow.org/" rel="nofollow">mlflow</a> allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:</p> <ul dir="auto"> <li>the package <code>mlfow</code> installed in your virtual env. If not you can install it with</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ pip install mlflow"><pre class="notranslate"><code>$ pip install mlflow </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Creating a <code>MLFlowCallback</code></h3><a id="user-content-creating-a-mlflowcallback" class="anchor" aria-label="Permalink: Creating a MLFlowCallback" href="#creating-a-mlflowcallback"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Launching an experiment monitoring with <code>mlfow</code> in pythae is pretty simple. The only thing a user needs to do is create a <code>MLFlowCallback</code> instance...</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> # Create you callback >>> from pythae.trainers.training_callbacks import MLFlowCallback >>> callbacks = [] # the TrainingPipeline expects a list of callbacks >>> mlflow_cb = MLFlowCallback() # Build the callback >>> # SetUp the callback >>> mlflow_cb.setup( ... training_config=your_training_config, # training config ... model_config=your_model_config, # model config ... run_name="mlflow_cb_example", # specify your mlflow run ... ) >>> callbacks.append(mlflow_cb) # Add it to the callbacks list"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Create you callback</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">trainers</span>.<span class="pl-s1">training_callbacks</span> <span class="pl-k">import</span> <span class="pl-v">MLFlowCallback</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">callbacks</span> <span class="pl-c1">=</span> [] <span class="pl-c"># the TrainingPipeline expects a list of callbacks</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">mlflow_cb</span> <span class="pl-c1">=</span> <span class="pl-v">MLFlowCallback</span>() <span class="pl-c"># Build the callback </span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># SetUp the callback </span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">mlflow_cb</span>.<span class="pl-en">setup</span>( ... <span class="pl-s1">training_config</span><span class="pl-c1">=</span><span class="pl-s1">your_training_config</span>, <span class="pl-c"># training config</span> ... <span class="pl-s1">model_config</span><span class="pl-c1">=</span><span class="pl-s1">your_model_config</span>, <span class="pl-c"># model config</span> ... <span class="pl-s1">run_name</span><span class="pl-c1">=</span><span class="pl-s">"mlflow_cb_example"</span>, <span class="pl-c"># specify your mlflow run</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">callbacks</span>.<span class="pl-en">append</span>(<span class="pl-s1">mlflow_cb</span>) <span class="pl-c"># Add it to the callbacks list</span></pre></div> <p dir="auto">...and then pass it to the <code>TrainingPipeline</code>.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> pipeline = TrainingPipeline( ... training_config=config, ... model=model ... ) >>> pipeline( ... train_data=train_dataset, ... eval_data=eval_dataset, ... callbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done! ... )"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">pipeline</span> <span class="pl-c1">=</span> <span class="pl-v">TrainingPipeline</span>( ... <span class="pl-s1">training_config</span><span class="pl-c1">=</span><span class="pl-s1">config</span>, ... <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">model</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-en">pipeline</span>( ... <span class="pl-s1">train_data</span><span class="pl-c1">=</span><span class="pl-s1">train_dataset</span>, ... <span class="pl-s1">eval_data</span><span class="pl-c1">=</span><span class="pl-s1">eval_dataset</span>, ... <span class="pl-s1">callbacks</span><span class="pl-c1">=</span><span class="pl-s1">callbacks</span> <span class="pl-c"># pass the callbacks to the TrainingPipeline and you are done!</span> ... )</pre></div> <p dir="auto">you can visualize your metric by running the following in the directory where the <code>./mlruns</code></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ mlflow ui "><pre>$ mlflow ui </pre></div> <p dir="auto">See the detailed tutorial</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Monitoring your experiments with <code>comet_ml</code> 🧪</h2><a id="user-content-monitoring-your-experiments-with-comet_ml-" class="anchor" aria-label="Permalink: Monitoring your experiments with comet_ml 🧪" href="#monitoring-your-experiments-with-comet_ml-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pythae also integrates the experiment tracking tool <a href="https://www.comet.com/signup?utm_source=pythae&utm_medium=partner&utm_campaign=AMS_US_EN_SNUP_Pythae_Comet_Integration" rel="nofollow">comet_ml</a> allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:</p> <ul dir="auto"> <li>the package <code>comet_ml</code> installed in your virtual env. If not you can install it with</li> </ul> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ pip install comet_ml"><pre class="notranslate"><code>$ pip install comet_ml </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Creating a <code>CometCallback</code></h3><a id="user-content-creating-a-cometcallback" class="anchor" aria-label="Permalink: Creating a CometCallback" href="#creating-a-cometcallback"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Launching an experiment monitoring with <code>comet_ml</code> in pythae is pretty simple. The only thing a user needs to do is create a <code>CometCallback</code> instance...</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> # Create you callback >>> from pythae.trainers.training_callbacks import CometCallback >>> callbacks = [] # the TrainingPipeline expects a list of callbacks >>> comet_cb = CometCallback() # Build the callback >>> # SetUp the callback >>> comet_cb.setup( ... training_config=training_config, # training config ... model_config=model_config, # model config ... api_key="your_comet_api_key", # specify your comet api-key ... project_name="your_comet_project", # specify your wandb project ... #offline_run=True, # run in offline mode ... #offline_directory='my_offline_runs' # set the directory to store the offline runs ... ) >>> callbacks.append(comet_cb) # Add it to the callbacks list"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># Create you callback</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-k">from</span> <span class="pl-s1">pythae</span>.<span class="pl-s1">trainers</span>.<span class="pl-s1">training_callbacks</span> <span class="pl-k">import</span> <span class="pl-v">CometCallback</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">callbacks</span> <span class="pl-c1">=</span> [] <span class="pl-c"># the TrainingPipeline expects a list of callbacks</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">comet_cb</span> <span class="pl-c1">=</span> <span class="pl-v">CometCallback</span>() <span class="pl-c"># Build the callback </span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># SetUp the callback </span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">comet_cb</span>.<span class="pl-en">setup</span>( ... <span class="pl-s1">training_config</span><span class="pl-c1">=</span><span class="pl-s1">training_config</span>, <span class="pl-c"># training config</span> ... <span class="pl-s1">model_config</span><span class="pl-c1">=</span><span class="pl-s1">model_config</span>, <span class="pl-c"># model config</span> ... <span class="pl-s1">api_key</span><span class="pl-c1">=</span><span class="pl-s">"your_comet_api_key"</span>, <span class="pl-c"># specify your comet api-key</span> ... <span class="pl-s1">project_name</span><span class="pl-c1">=</span><span class="pl-s">"your_comet_project"</span>, <span class="pl-c"># specify your wandb project</span> ... <span class="pl-c">#offline_run=True, # run in offline mode</span> ... <span class="pl-c">#offline_directory='my_offline_runs' # set the directory to store the offline runs</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">callbacks</span>.<span class="pl-en">append</span>(<span class="pl-s1">comet_cb</span>) <span class="pl-c"># Add it to the callbacks list</span></pre></div> <p dir="auto">...and then pass it to the <code>TrainingPipeline</code>.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=">>> pipeline = TrainingPipeline( ... training_config=config, ... model=model ... ) >>> pipeline( ... train_data=train_dataset, ... eval_data=eval_dataset, ... callbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done! ... ) >>> # You can log to https://comet.com/your_comet_username/your_comet_project to monitor your training"><pre><span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">pipeline</span> <span class="pl-c1">=</span> <span class="pl-v">TrainingPipeline</span>( ... <span class="pl-s1">training_config</span><span class="pl-c1">=</span><span class="pl-s1">config</span>, ... <span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s1">model</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-en">pipeline</span>( ... <span class="pl-s1">train_data</span><span class="pl-c1">=</span><span class="pl-s1">train_dataset</span>, ... <span class="pl-s1">eval_data</span><span class="pl-c1">=</span><span class="pl-s1">eval_dataset</span>, ... <span class="pl-s1">callbacks</span><span class="pl-c1">=</span><span class="pl-s1">callbacks</span> <span class="pl-c"># pass the callbacks to the TrainingPipeline and you are done!</span> ... ) <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-c"># You can log to https://comet.com/your_comet_username/your_comet_project to monitor your training</span></pre></div> <p dir="auto">See the detailed tutorial</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting your hands on the code</h2><a id="user-content-getting-your-hands-on-the-code" class="anchor" aria-label="Permalink: Getting your hands on the code" href="#getting-your-hands-on-the-code"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To help you to understand the way pythae works and how you can train your models with this library we also provide tutorials:</p> <ul dir="auto"> <li> <p dir="auto"><a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks">making_your_own_autoencoder.ipynb</a> shows you how to pass your own networks to the models implemented in pythae <a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/making_your_own_autoencoder.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></p> </li> <li> <p dir="auto"><a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks">custom_dataset.ipynb</a> shows you how to use custom datasets with any of the models implemented in pythae <a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/custom_dataset.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></p> </li> <li> <p dir="auto"><a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks">hf_hub_models_sharing.ipynb</a> shows you how to upload and download models for the HuggingFace Hub <a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/hf_hub_models_sharing.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></p> </li> <li> <p dir="auto"><a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks">wandb_experiment_monitoring.ipynb</a> shows you how to monitor you experiments using <code>wandb</code> <a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/wandb_experiment_monitoring.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></p> </li> <li> <p dir="auto"><a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks">mlflow_experiment_monitoring.ipynb</a> shows you how to monitor you experiments using <code>mlflow</code> <a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/mlflow_experiment_monitoring.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></p> </li> <li> <p dir="auto"><a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks">comet_experiment_monitoring.ipynb</a> shows you how to monitor you experiments using <code>comet_ml</code> <a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/comet_experiment_monitoring.ipynb" rel="nofollow"><img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a></p> </li> <li> <p dir="auto"><a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks/models_training">models_training</a> folder provides notebooks showing how to train each implemented model and how to sample from it using <code>pythae.samplers</code>.</p> </li> <li> <p dir="auto"><a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts">scripts</a> folder provides in particular an example of a training script to train the models on benchmark data sets (mnist, cifar10, celeba ...)</p> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Dealing with issues 🛠️</h2><a id="user-content-dealing-with-issues-️" class="anchor" aria-label="Permalink: Dealing with issues 🛠️" href="#dealing-with-issues-️"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you are experiencing any issues while running the code or request new features/models to be implemented please <a href="https://github.com/clementchadebec/benchmark_VAE/issues">open an issue on github</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributing 🚀</h2><a id="user-content-contributing-" class="anchor" aria-label="Permalink: Contributing 🚀" href="#contributing-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You want to contribute to this library by adding a model, a sampler or simply fix a bug ? That's awesome! Thank you! Please see <a href="https://github.com/clementchadebec/benchmark_VAE/tree/main/CONTRIBUTING.md">CONTRIBUTING.md</a> to follow the main contributing guidelines.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Results</h2><a id="user-content-results" class="anchor" aria-label="Permalink: Results" href="#results"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Reconstruction</h3><a id="user-content-reconstruction" class="anchor" aria-label="Permalink: Reconstruction" href="#reconstruction"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">First let's have a look at the reconstructed samples taken from the evaluation set.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Models</th> <th align="center">MNIST</th> <th align="center">CELEBA</th> </tr> </thead> <tbody> <tr> <td align="center">Eval data</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/eval_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/eval_reconstruction_mnist.png" alt="Eval" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/eval_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/eval_reconstruction_celeba.png" alt="AE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">AE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/ae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/ae_reconstruction_mnist.png" alt="AE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/ae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/ae_reconstruction_celeba.png" alt="AE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_reconstruction_mnist.png" alt="VAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_reconstruction_celeba.png" alt="VAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">Beta-VAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_vae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_vae_reconstruction_mnist.png" alt="Beta" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_vae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_vae_reconstruction_celeba.png" alt="Beta Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE Lin NF</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_lin_nf_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_lin_nf_reconstruction_mnist.png" alt="VAE_LinNF" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_lin_nf_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_lin_nf_reconstruction_celeba.png" alt="VAE_IAF Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE IAF</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_iaf_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_iaf_reconstruction_mnist.png" alt="VAE_IAF" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_iaf_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_iaf_reconstruction_celeba.png" alt="VAE_IAF Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">Disentangled Beta-VAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/disentangled_beta_vae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/disentangled_beta_vae_reconstruction_mnist.png" alt="Disentangled Beta" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/disentangled_beta_vae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/disentangled_beta_vae_reconstruction_celeba.png" alt="Disentangled Beta" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">FactorVAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/factor_vae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/factor_vae_reconstruction_mnist.png" alt="FactorVAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/factor_vae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/factor_vae_reconstruction_celeba.png" alt="FactorVAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">BetaTCVAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_tc_vae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_tc_vae_reconstruction_mnist.png" alt="BetaTCVAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_tc_vae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_tc_vae_reconstruction_celeba.png" alt="BetaTCVAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">IWAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/iwae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/iwae_reconstruction_mnist.png" alt="IWAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/iwae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/iwae_reconstruction_celeba.png" alt="IWAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">MSSSIM_VAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/msssim_vae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/msssim_vae_reconstruction_mnist.png" alt="MSSSIM VAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/msssim_vae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/msssim_vae_reconstruction_celeba.png" alt="MSSSIM VAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">WAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/wae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/wae_reconstruction_mnist.png" alt="WAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/wae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/wae_reconstruction_celeba.png" alt="WAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">INFO VAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/infovae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/infovae_reconstruction_mnist.png" alt="INFO" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/infovae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/infovae_reconstruction_celeba.png" alt="INFO" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAMP</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vamp_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vamp_reconstruction_mnist.png" alt="VAMP" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vamp_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vamp_reconstruction_celeba.png" alt="VAMP" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">SVAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/svae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/svae_reconstruction_mnist.png" alt="SVAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/svae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/svae_reconstruction_celeba.png" alt="SVAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">Adversarial_AE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/aae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/aae_reconstruction_mnist.png" alt="AAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/aae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/aae_reconstruction_celeba.png" alt="AAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE_GAN</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vaegan_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vaegan_reconstruction_mnist.png" alt="VAEGAN" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vaegan_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vaegan_reconstruction_celeba.png" alt="VAEGAN" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VQVAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vqvae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vqvae_reconstruction_mnist.png" alt="VQVAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vqvae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vqvae_reconstruction_celeba.png" alt="VQVAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">HVAE</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/hvae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/hvae_reconstruction_mnist.png" alt="HVAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/hvae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/hvae_reconstruction_celeba.png" alt="HVAE" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">RAE_L2</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_l2_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_l2_reconstruction_mnist.png" alt="RAE L2" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_l2_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_l2_reconstruction_celeba.png" alt="RAE L2" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">RAE_GP</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_gp_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_gp_reconstruction_mnist.png" alt="RAE GMM" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_gp_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_gp_reconstruction_celeba.png" alt="RAE GMM" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">Riemannian Hamiltonian VAE (RHVAE)</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rhvae_reconstruction_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rhvae_reconstruction_mnist.png" alt="RHVAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rhvae_reconstruction_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rhvae_reconstruction_celeba.png" alt="RHVAE RHVAE" style="max-width: 100%;"></a></td> </tr> </tbody> </table></markdown-accessiblity-table> <hr> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Generation</h3><a id="user-content-generation" class="anchor" aria-label="Permalink: Generation" href="#generation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Here, we show the generated samples using each model implemented in the library and different samplers.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center">Models</th> <th align="center">MNIST</th> <th align="center">CELEBA</th> </tr> </thead> <tbody> <tr> <td align="center">AE + GaussianMixtureSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/ae_gmm_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/ae_gmm_sampling_mnist.png" alt="AE GMM" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/ae_gmm_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/ae_gmm_sampling_celeba.png" alt="AE GMM" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_normal_sampling_mnist.png" alt="VAE Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_normal_sampling_celeba.png" alt="VAE Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE + GaussianMixtureSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_gmm_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_gmm_sampling_mnist.png" alt="VAE GMM" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_gmm_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_gmm_sampling_celeba.png" alt="VAE GMM" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE + TwoStageVAESampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_second_stage_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_second_stage_sampling_mnist.png" alt="VAE 2 stage" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_second_stage_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_second_stage_sampling_celeba.png" alt="VAE 2 stage" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE + MAFSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_maf_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_maf_sampling_mnist.png" alt="VAE MAF" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_maf_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_maf_sampling_celeba.png" alt="VAE MAF" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">Beta-VAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_vae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_vae_normal_sampling_mnist.png" alt="Beta Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_vae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_vae_normal_sampling_celeba.png" alt="Beta Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE Lin NF + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_lin_nf_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_lin_nf_normal_sampling_mnist.png" alt="VAE_LinNF Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_lin_nf_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_lin_nf_normal_sampling_celeba.png" alt="VAE_LinNF Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAE IAF + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_iaf_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_iaf_normal_sampling_mnist.png" alt="VAE_IAF Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vae_iaf_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vae_iaf_normal_sampling_celeba.png" alt="VAE IAF Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">Disentangled Beta-VAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/disentangled_beta_vae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/disentangled_beta_vae_normal_sampling_mnist.png" alt="Disentangled Beta Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/disentangled_beta_vae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/disentangled_beta_vae_normal_sampling_celeba.png" alt="Disentangled Beta Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">FactorVAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/factor_vae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/factor_vae_normal_sampling_mnist.png" alt="FactorVAE Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/factor_vae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/factor_vae_normal_sampling_celeba.png" alt="FactorVAE Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">BetaTCVAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_tc_vae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_tc_vae_normal_sampling_mnist.png" alt="BetaTCVAE Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/beta_tc_vae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/beta_tc_vae_normal_sampling_celeba.png" alt="BetaTCVAE Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">IWAE + Normal sampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/iwae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/iwae_normal_sampling_mnist.png" alt="IWAE Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/iwae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/iwae_normal_sampling_celeba.png" alt="IWAE Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">MSSSIM_VAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/msssim_vae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/msssim_vae_normal_sampling_mnist.png" alt="MSSSIM_VAE Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/msssim_vae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/msssim_vae_normal_sampling_celeba.png" alt="MSSSIM_VAE Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">WAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/wae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/wae_normal_sampling_mnist.png" alt="WAE Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/wae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/wae_normal_sampling_celeba.png" alt="WAE Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">INFO VAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/infovae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/infovae_normal_sampling_mnist.png" alt="INFO Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/infovae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/infovae_normal_sampling_celeba.png" alt="INFO Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">SVAE + HypershereUniformSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/svae_hypersphere_uniform_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/svae_hypersphere_uniform_sampling_mnist.png" alt="SVAE Sphere" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/svae_hypersphere_uniform_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/svae_hypersphere_uniform_sampling_celeba.png" alt="SVAE Sphere" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAMP + VAMPSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vamp_vamp_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vamp_vamp_sampling_mnist.png" alt="VAMP Vamp" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vamp_vamp_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vamp_vamp_sampling_celeba.png" alt="VAMP Vamp" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">Adversarial_AE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/aae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/aae_normal_sampling_mnist.png" alt="AAE_Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/aae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/aae_normal_sampling_celeba.png" alt="AAE_Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VAEGAN + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vaegan_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vaegan_normal_sampling_mnist.png" alt="VAEGAN_Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vaegan_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vaegan_normal_sampling_celeba.png" alt="VAEGAN_Normal" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">VQVAE + MAFSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vqvae_maf_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vqvae_maf_sampling_mnist.png" alt="VQVAE_MAF" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/vqvae_maf_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/vqvae_maf_sampling_celeba.png" alt="VQVAE_MAF" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">HVAE + NormalSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/hvae_normal_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/hvae_normal_sampling_mnist.png" alt="HVAE Normal" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/hvae_normal_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/hvae_normal_sampling_celeba.png" alt="HVAE GMM" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">RAE_L2 + GaussianMixtureSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_l2_gmm_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_l2_gmm_sampling_mnist.png" alt="RAE L2 GMM" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_l2_gmm_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_l2_gmm_sampling_celeba.png" alt="RAE L2 GMM" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">RAE_GP + GaussianMixtureSampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_gp_gmm_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_gp_gmm_sampling_mnist.png" alt="RAE GMM" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rae_gp_gmm_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rae_gp_gmm_sampling_celeba.png" alt="RAE GMM" style="max-width: 100%;"></a></td> </tr> <tr> <td align="center">Riemannian Hamiltonian VAE (RHVAE) + RHVAE Sampler</td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rhvae_rhvae_sampling_mnist.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rhvae_rhvae_sampling_mnist.png" alt="RHVAE RHVAE" style="max-width: 100%;"></a></td> <td align="center"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/showcases/rhvae_rhvae_sampling_celeba.png"><img src="https://github.com/clementchadebec/benchmark_VAE/raw/main/examples/showcases/rhvae_rhvae_sampling_celeba.png" alt="RHVAE RHVAE" style="max-width: 100%;"></a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Citation</h1><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you find this work useful or use it in your research, please consider citing us</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@inproceedings{chadebec2022pythae, author = {Chadebec, Cl\'{e}ment and Vincent, Louis and Allassonniere, Stephanie}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {21575--21589}, publisher = {Curran Associates, Inc.}, title = {Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case}, volume = {35}, year = {2022} }"><pre><span class="pl-k">@inproceedings</span>{<span class="pl-en">chadebec2022pythae</span>, <span class="pl-s">author</span> = <span class="pl-s"><span class="pl-pds">{</span>Chadebec, Cl\'{e}ment and Vincent, Louis and Allassonniere, Stephanie<span class="pl-pds">}</span></span>, <span class="pl-s">booktitle</span> = <span class="pl-s"><span class="pl-pds">{</span>Advances in Neural Information Processing Systems<span class="pl-pds">}</span></span>, <span class="pl-s">editor</span> = <span class="pl-s"><span class="pl-pds">{</span>S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh<span class="pl-pds">}</span></span>, <span class="pl-s">pages</span> = <span class="pl-s"><span class="pl-pds">{</span>21575--21589<span class="pl-pds">}</span></span>, <span class="pl-s">publisher</span> = <span class="pl-s"><span class="pl-pds">{</span>Curran Associates, Inc.<span class="pl-pds">}</span></span>, <span class="pl-s">title</span> = <span class="pl-s"><span class="pl-pds">{</span>Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case<span class="pl-pds">}</span></span>, <span class="pl-s">volume</span> = <span class="pl-s"><span class="pl-pds">{</span>35<span class="pl-pds">}</span></span>, <span class="pl-s">year</span> = <span class="pl-s"><span class="pl-pds">{</span>2022<span class="pl-pds">}</span></span> }</pre></div> </article></div></div></div></div></div>   <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" 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