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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 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 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" 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2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon 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0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center 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role="listbox" class="ActionListWrap QueryBuilder-ListWrap" aria-label="Suggestions" data-action=" combobox-commit:query-builder#comboboxCommit mousedown:query-builder#resultsMousedown " data-target="query-builder.resultsList" data-persist-list=false id="query-builder-test-results" ></ul> </div> <div class="FormControl-inlineValidation" id="validation-f98bd70d-c395-4897-afc0-748d5a879f28" hidden="hidden"> <span class="FormControl-inlineValidation--visual"> <svg aria-hidden="true" height="12" viewBox="0 0 12 12" version="1.1" width="12" data-view-component="true" class="octicon octicon-alert-fill"> <path d="M4.855.708c.5-.896 1.79-.896 2.29 0l4.675 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 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--></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="psFrhIxybvRofNme7VRoPxCF4knJMaX4eYIpgsQ/zjAvkQByj9ifL/YcNE+xqCPaxs+jgplMEHm7FdpKy0nzbQ==" /> <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 Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-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="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="wYdfeyB/LKsKfrTYD4hD21bSNll07Oc8/jFbbusNwP66NuxkZ8Gjp4Cbdvg1OLJEww2EiyuA7uVn6vo6KrwE6Q==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" id="custom_scope_id" name="custom_scope_id" data-target="custom-scopes.customScopesIdField"> <div class="form-group"> <label for="custom_scope_name">Name</label> <auto-check src="/search/custom_scopes/check_name" required only-validate-on-blur="false"> <input type="text" name="custom_scope_name" id="custom_scope_name" data-target="custom-scopes.customScopesNameField" class="form-control" autocomplete="off" placeholder="github-ruby" required maxlength="50"> <input type="hidden" data-csrf="true" value="RFLDF99nG967MipiYSIBNygxtJUX0v9z0/w0iT+JDucaZJcEq6E9Kp9UXFfxv06/t9JLZjuJTewHslHFzrUfxg==" /> </auto-check> </div> <div class="form-group"> <label for="custom_scope_query">Query</label> <input type="text" name="custom_scope_query" id="custom_scope_query" data-target="custom-scopes.customScopesQueryField" class="form-control" autocomplete="off" placeholder="(repo:mona/a OR repo:mona/b) AND lang:python" required maxlength="500"> </div> <p class="text-small color-fg-muted"> To see all available qualifiers, see our <a class="Link--inTextBlock" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax">documentation</a>. </p> </form> </div> <div data-target="custom-scopes.manageCustomScopesForm"> <div data-target="custom-scopes.list"></div> </div> </div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd Overlay-footer--divided"> <button data-action="click:custom-scopes#customScopesCancel" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="custom-scopes-dialog-form" data-action="click:custom-scopes#customScopesSubmit" data-target="custom-scopes.customScopesSubmitButton" type="submit" data-view-component="true" class="btn-primary btn"> Create saved search </button> </div> </dialog></dialog-helper> </custom-scopes> </div> </qbsearch-input> <div class="position-relative HeaderMenu-link-wrap d-lg-inline-block"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2FDeepVAC%2Fdeepvac" class="HeaderMenu-link HeaderMenu-link--sign-in HeaderMenu-button flex-shrink-0 no-underline d-none d-lg-inline-flex border border-lg-0 rounded rounded-lg-0 px-2 py-1" style="margin-left: 12px;" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/DeepVAC/deepvac","user_id":null}}" 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dir=\"auto\"\u003eDeepVAC\u003c/h1\u003e\u003ca id=\"user-content-deepvac\" class=\"anchor\" aria-label=\"Permalink: DeepVAC\" href=\"#deepvac\"\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\"\u003eDeepVAC提供了基于PyTorch的AI项目的工程化规范。为了达到这一目标，DeepVAC包含了：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e软件工程规范：\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/arch.md\"\u003e软件工程规范\u003c/a\u003e；\u003c/li\u003e\n\u003cli\u003e代码规范：\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/code_standard.md\"\u003e代码规范\u003c/a\u003e；\u003c/li\u003e\n\u003cli\u003edeepvac库：\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/lib.md\"\u003edeepvac库\u003c/a\u003e。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e诸多PyTorch AI项目的内在逻辑都大同小异，因此DeepVAC致力于把更通用的逻辑剥离出来，从而使得工程代码的准确性、易读性、可维护性上更具优势。\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e如果想使得AI项目符合DeepVAC规范，需要仔细阅读\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/deepvac_standard.md\"\u003eDeepVAC标准\u003c/a\u003e。\n如果想了解deepvac库的设计，请阅读\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/design.md\"\u003edeepvac库的设计\u003c/a\u003e。\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e如何基于DeepVAC构建自己的PyTorch AI项目\u003c/h1\u003e\u003ca id=\"user-content-如何基于deepvac构建自己的pytorch-ai项目\" class=\"anchor\" aria-label=\"Permalink: 如何基于DeepVAC构建自己的PyTorch AI项目\" href=\"#如何基于deepvac构建自己的pytorch-ai项目\"\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\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1. 阅读\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/deepvac_standard.md\"\u003eDeepVAC标准\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-1-阅读deepvac标准\" class=\"anchor\" aria-label=\"Permalink: 1. 阅读DeepVAC标准\" href=\"#1-阅读deepvac标准\"\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\"\u003e可以粗略阅读，建立起第一印象。\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2. 环境准备\u003c/h2\u003e\u003ca id=\"user-content-2-环境准备\" class=\"anchor\" aria-label=\"Permalink: 2. 环境准备\" href=\"#2-环境准备\"\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\"\u003eDeepVAC的依赖有：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePython3。不支持Python2，其已被废弃；\u003c/li\u003e\n\u003cli\u003e依赖包：torch, torchvision, tensorboard, scipy, numpy, cv2, Pillow；\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e这些依赖使用pip命令（或者git clone）自行安装，不再赘述。\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e对于普通用户来说，最方便高效的方式还是使用\u003ca href=\"https://github.com/DeepVAC/MLab#mlab-homepod\"\u003eMLab HomePod\u003c/a\u003e作为DeepVAC的使用环境，这是一个预构建的Docker image，可以帮助用户省掉不必要的环境配置时间。\n同时在MLab组织内部，我们也使用\u003ca href=\"https://github.com/DeepVAC/MLab#mlab-homepod\"\u003eMLab HomePod\u003c/a\u003e进行日常的模型的训练任务。\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3. 安装deepvac库\u003c/h2\u003e\u003ca id=\"user-content-3-安装deepvac库\" class=\"anchor\" aria-label=\"Permalink: 3. 安装deepvac库\" href=\"#3-安装deepvac库\"\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\"\u003e可以使用pip来进行安装：\u003cbr\u003e\n\u003ccode\u003epip3 install deepvac\u003c/code\u003e\u003cbr\u003e\n或者\u003cbr\u003e\n\u003ccode\u003epython3 -m pip install deepvac\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e如果你需要使用deepvac在github上的最新代码，就需要使用如下的开发者模式：\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e开发者模式\u003c/h4\u003e\u003ca id=\"user-content-开发者模式\" class=\"anchor\" aria-label=\"Permalink: 开发者模式\" href=\"#开发者模式\"\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克隆该项目到本地：\u003ccode\u003egit clone https://github.com/DeepVAC/deepvac \u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e在你的入口文件中添加：\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import sys\n#replace with your local deepvac directory\nsys.path.insert(0,'/home/gemfield/github/deepvac')\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esys\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e#replace with your local deepvac directory\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esys\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epath\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003einsert\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'/home/gemfield/github/deepvac'\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e或者设置PYTHONPATH环境变量：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export PYTHONPATH=/home/gemfield/github/deepvac\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PYTHONPATH=/home/gemfield/github/deepvac\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e4. 创建自己的PyTorch项目\u003c/h2\u003e\u003ca id=\"user-content-4-创建自己的pytorch项目\" class=\"anchor\" aria-label=\"Permalink: 4. 创建自己的PyTorch项目\" href=\"#4-创建自己的pytorch项目\"\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初始化自己项目的git仓库；\u003c/li\u003e\n\u003cli\u003e在仓库中创建第一个研究分支，比如分支名为 LTS_b1_aug9_movie_video_plate_130w；\u003c/li\u003e\n\u003cli\u003e切换到上述的LTS_b1分支中，开始工作；\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e5. 编写配置文件\u003c/h2\u003e\u003ca id=\"user-content-5-编写配置文件\" class=\"anchor\" aria-label=\"Permalink: 5. 编写配置文件\" href=\"#5-编写配置文件\"\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\"\u003e配置文件的文件名均为 config.py，位于你项目的根目录。在代码开始处添加\u003ccode\u003efrom deepvac import new, AttrDict\u003c/code\u003e；\n所有用户的配置都存放在这个文件里。config模块提供了6个预定义的作用域：config.core,config.aug,config.cast,config.datasets,config.backbones,config.loss。使用方法如下：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e所有和trainer相关（包括train、val、test）的配置都定义在config.core.\u0026lt;my_train_class\u0026gt;中；\u003c/li\u003e\n\u003cli\u003e所有和deepvac.aug中增强模块相关的配置都定义在config.aug.\u0026lt;my_aug_class\u0026gt;中；\u003c/li\u003e\n\u003cli\u003e所有和模型转换相关的配置都定义在config.cast.\u0026lt;the_caster_class\u0026gt;中；\u003c/li\u003e\n\u003cli\u003e所有和Datasets相关的配置都定义在config.datasets.\u0026lt;my_dataset_class\u0026gt;中；\u003c/li\u003e\n\u003cli\u003e所有和loss相关的配置都定义在config.loss.\u0026lt;my_loss_class\u0026gt;中；\u003c/li\u003e\n\u003cli\u003e用户可以开辟自己的作用域，比如config.my_stuff = AttrDict()，然后config.my_stuff.name = 'gemfield'；\u003c/li\u003e\n\u003cli\u003e用户可以使用new()来初始化config实例，使用clone()来深拷贝config配置项。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e更多配置：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e预训练模型加载；\u003c/li\u003e\n\u003cli\u003echeckpoint加载；\u003c/li\u003e\n\u003cli\u003etensorboard使用；\u003c/li\u003e\n\u003cli\u003eTorchScript使用；\u003c/li\u003e\n\u003cli\u003e转换ONNX；\u003c/li\u003e\n\u003cli\u003e转换NCNN；\u003c/li\u003e\n\u003cli\u003e转换CoreML；\u003c/li\u003e\n\u003cli\u003e转换TensorRT；\u003c/li\u003e\n\u003cli\u003e转换TNN；\u003c/li\u003e\n\u003cli\u003e转换MNN；\u003c/li\u003e\n\u003cli\u003e开启量化；\u003c/li\u003e\n\u003cli\u003e开启EMA；\u003c/li\u003e\n\u003cli\u003e开启自动混合精度训练(AMP)；\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e以及关于配置文件的更详细解释，请阅读\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/config.md\"\u003econfig说明\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e项目根目录下的train.py中用如下方式引用config.py文件:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from config import config as deepvac_config\nfrom deepvac import DeepvacTrain\n\nclass MyTrain(DeepvacTrain):\n ......\n\nmy_train = MyTrain(deepvac_config)\nmy_train()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac_config\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDeepvacTrain\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyTrain\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eDeepvacTrain\u003c/span\u003e):\n ......\n\n\u003cspan class=\"pl-s1\"\u003emy_train\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMyTrain\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edeepvac_config\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003emy_train\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e项目根目录下的test.py中用如下方式引用config.py文件:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from config import config as deepvac_config\nfrom deepvac import Deepvac\n\nclass MyTest(Deepvac)\n ......\n\nmy_test = MyTest(deepvac_config)\nmy_test()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac_config\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDeepvac\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyTest\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eDeepvac\u003c/span\u003e)\n ......\n\n\u003cspan class=\"pl-s1\"\u003emy_test\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyTest\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edeepvac_config\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003emy_test\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e之后，train.py/test.py代码中通过如下方式来读写config.core中的配置项\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"print(self.config.log_dir)\nprint(self.config.batch_size)\n......\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elog_dir\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebatch_size\u003c/span\u003e)\n......\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e此外，鉴于config的核心作用，deepvac还设计了如下的API来方便对config模块的使用：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAttrDict\u003c/li\u003e\n\u003cli\u003enew\u003c/li\u003e\n\u003cli\u003einterpret\u003c/li\u003e\n\u003cli\u003efork\u003c/li\u003e\n\u003cli\u003eclone\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from deepvac import AttrDict, new, interpret, fork\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAttrDict\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enew\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einterpret\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003efork\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e关于这些API的使用方法，请访问\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/design.md#config-module\"\u003econfig API 说明\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e6. 编写synthesis/synthesis.py（可选）\u003c/h2\u003e\u003ca id=\"user-content-6-编写synthesissynthesispy可选\" class=\"anchor\" aria-label=\"Permalink: 6. 编写synthesis/synthesis.py（可选）\" href=\"#6-编写synthesissynthesispy可选\"\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\"\u003e编写该文件，用于产生本项目的数据集，用于对本项目的数据集进行自动化检查和清洗。\n这一步为可选，如果有需要的话，可以参考Deepvac组织下Synthesis2D项目的实现。\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e7. 编写aug/aug.py（可选）\u003c/h2\u003e\u003ca id=\"user-content-7-编写augaugpy可选\" class=\"anchor\" aria-label=\"Permalink: 7. 编写aug/aug.py（可选）\" href=\"#7-编写augaugpy可选\"\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\"\u003e编写该文件，用于实现数据增强策略。\ndeepvac.aug模块为数据增强设计了特有的语法，在两个层面实现了复用：aug 和 composer。比如说，我想复用添加随机斑点的SpeckleAug：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from deepvac.aug.base_aug import SpeckleAug\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eaug\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ebase_aug\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSpeckleAug\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e这是对底层aug算子的复用。我们还可以直接复用别人写好的composer，并且是以直截了当的方式。比如deepvac.aug提供了一个用于人脸检测数据增强的RetinaAugComposer：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from deepvac.aug import RetinaAugComposer\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eaug\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eRetinaAugComposer\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e以上说的是直接复用，但项目中更多的是自定义扩展，而且大部分情况下也需要复用torchvision的transform的compose，又该怎么办呢？这里解释下，composer是deepvac.aug模块的概念，compose是torchvision transform模块的概念，之所以这么相似纯粹是因为巧合。\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e要扩展自己的composer也是很简单的，比如我可以自定义一个composer（我把它命名为GemfieldComposer），这个composer可以使用/复用以下增强逻辑：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003etorchvision transform定义的compose；\u003c/li\u003e\n\u003cli\u003edeepvac内置的aug算子；\u003c/li\u003e\n\u003cli\u003e我自己写的aug算子。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e更详细的步骤请访问：\u003ca href=\"/DeepVAC/deepvac/blob/master/docs/design.md#aug\"\u003edeepvac.aug模块使用\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e8. 编写Dataset类\u003c/h2\u003e\u003ca id=\"user-content-8-编写dataset类\" class=\"anchor\" aria-label=\"Permalink: 8. 编写Dataset类\" href=\"#8-编写dataset类\"\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\"\u003e代码编写在data/dataloader.py文件中。继承deepvac.datasets类体系，比如FileLineDataset类提供了对如下train.txt这种格式的封装：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"#train.txt，第一列为图片路径，第二列为label\nimg0/1.jpg 0\nimg0/2.jpg 0\n...\nimg1/0.jpg 1\n...\nimg2/0.jpg 2\n...\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003etrain.txt，第一列为图片路径，第二列为label\u003c/span\u003e\nimg0/1.jpg 0\nimg0/2.jpg 0\n...\nimg1/0.jpg 1\n...\nimg2/0.jpg 2\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e有时第二列是字符串，并且想把FileLineDataset中使用Image读取图片对方式替换为cv2，那么可以通过如下的继承方式来重新实现：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from deepvac.datasets import FileLineDataset\n\nclass FileLineCvStrDataset(FileLineDataset):\n def _buildLabelFromLine(self, line):\n line = line.strip().split(\u0026quot; \u0026quot;)\n return [line[0], line[1]]\n\n def _buildSampleFromPath(self, abs_path):\n #we just set default loader with Pillow Image\n sample = cv2.imread(abs_path)\n sample = self.compose(sample)\n return sample\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eFileLineDataset\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eFileLineCvStrDataset\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eFileLineDataset\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e_buildLabelFromLine\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eline\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eline\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eline\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003estrip\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003esplit\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\" \"\u003c/span\u003e)\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e [\u003cspan class=\"pl-s1\"\u003eline\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003eline\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e]]\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e_buildSampleFromPath\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eabs_path\u003c/span\u003e):\n \u003cspan class=\"pl-c\"\u003e#we just set default loader with Pillow Image\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003esample\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecv2\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eimread\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eabs_path\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003esample\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecompose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esample\u003c/span\u003e)\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esample\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e哦，FileLineCvStrDataset也已经是deepvac.datasets中提供的类了。\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e9. 编写训练和验证脚本\u003c/h2\u003e\u003ca id=\"user-content-9-编写训练和验证脚本\" class=\"anchor\" aria-label=\"Permalink: 9. 编写训练和验证脚本\" href=\"#9-编写训练和验证脚本\"\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\"\u003e在Deepvac规范中，train.py就代表了训练范式。模型训练的代码写在train.py文件中，继承DeepvacTrain类：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from deepvac import DeepvacTrain\n\nclass MyTrain(DeepvacTrain):\n pass\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edeepvac\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eDeepvacTrain\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyTrain\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eDeepvacTrain\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003epass\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e继承DeepvacTrain的子类可能需要重新实现以下方法才能够开始训练：\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e类的方法（*号表示用户一般要重新实现）\u003c/th\u003e\n\u003cth\u003e功能\u003c/th\u003e\n\u003cth\u003e备注\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003epreEpoch\u003c/td\u003e\n\u003ctd\u003e每轮Epoch之前的用户操作，DeepvacTrain啥也不做\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epreIter\u003c/td\u003e\n\u003ctd\u003e每个batch迭代之前的用户操作，DeepvacTrain啥也不做\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epostIter\u003c/td\u003e\n\u003ctd\u003e每个batch迭代之后的用户操作，DeepvacTrain啥也不做\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epostEpoch\u003c/td\u003e\n\u003ctd\u003e每轮Epoch之后的用户操作，DeepvacTrain啥也不做\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoFeedData2Device\u003c/td\u003e\n\u003ctd\u003eDeepvacTrain把来自dataloader的sample和target(标签)移动到device设备上\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoForward\u003c/td\u003e\n\u003ctd\u003eDeepvacTrain会进行网络推理，推理结果赋值给self.config.output成员\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoLoss\u003c/td\u003e\n\u003ctd\u003eDeepvacTrain会使用self.config.output和self.config.target进行计算得到此次迭代的loss\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoBackward\u003c/td\u003e\n\u003ctd\u003e网络反向传播过程，DeepvacTrain会调用self.config.loss.backward()进行BP\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoOptimize\u003c/td\u003e\n\u003ctd\u003e网络权重更新的过程，DeepvacTrain会调用self.config.optimizer.step()\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoSchedule\u003c/td\u003e\n\u003ctd\u003e更新学习率的过程，DeepvacTrain会调用self.config.scheduler.step()\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e* doValAcc\u003c/td\u003e\n\u003ctd\u003e在val模式下计算模型的acc，DeepvacTrain啥也不做\u003c/td\u003e\n\u003ctd\u003e用户一般要重新定义，写tensorboard的时候依赖于此\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典型的写法如下：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"class MyTrain(DeepvacTrain):\n ...\n #因为基类不能处理list类型的标签，重写该方法\n def doFeedData2Device(self):\n self.config.target = [anno.to(self.config.device) for anno in self.config.target]\n self.config.sample = self.config.sample.to(self.config.device)\n\n #初始化config.core.acc\n def doValAcc(self):\n self.config.acc = your_acc\n LOG.logI('Test accuray: {:.4f}'.format(self.config.acc))\n\n\ntrain = MyTrain(deepvac_config)\ntrain()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyTrain\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eDeepvacTrain\u003c/span\u003e):\n ...\n \u003cspan class=\"pl-c\"\u003e#因为基类不能处理list类型的标签，重写该方法\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003edoFeedData2Device\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etarget\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s1\"\u003eanno\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edevice\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eanno\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etarget\u003c/span\u003e]\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esample\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esample\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edevice\u003c/span\u003e)\n\n \u003cspan class=\"pl-c\"\u003e#初始化config.core.acc\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003edoValAcc\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eacc\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eyour_acc\u003c/span\u003e\n \u003cspan class=\"pl-c1\"\u003eLOG\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elogI\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'Test accuray: {:.4f}'\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003econfig\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eacc\u003c/span\u003e))\n\n\n\u003cspan class=\"pl-s1\"\u003etrain\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMyTrain\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edeepvac_config\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003etrain\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\"\u003e10. 编写测试脚本\u003c/h2\u003e\u003ca id=\"user-content-10-编写测试脚本\" class=\"anchor\" aria-label=\"Permalink: 10. 编写测试脚本\" href=\"#10-编写测试脚本\"\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\"\u003e在Deepvac规范中，test.py就代表测试范式。测试代码写在test.py文件中，继承Deepvac类。\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e和train.py中的train/val的本质不同在于：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e舍弃train/val上下文；\u003c/li\u003e\n\u003cli\u003e网络不再使用autograd上下文；\u003c/li\u003e\n\u003cli\u003e不再进行loss、反向、优化等计算；\u003c/li\u003e\n\u003cli\u003e使用Deepvac的*Report模块来进行准确度、速度方面的衡量；\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e继承Deepvac类的子类必须（重新）实现以下方法才能够开始测试：\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e类的方法（*号表示必需重新实现）\u003c/th\u003e\n\u003cth\u003e功能\u003c/th\u003e\n\u003cth\u003e备注\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003epreIter\u003c/td\u003e\n\u003ctd\u003e每个batch迭代之前的用户操作，Deepvac啥也不做\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epostIter\u003c/td\u003e\n\u003ctd\u003e每个batch迭代之后的用户操作，Deepvac啥也不做\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoFeedData2Device\u003c/td\u003e\n\u003ctd\u003eDeepvac把来自dataloader的sample和target(标签)移动到device设备上\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoForward\u003c/td\u003e\n\u003ctd\u003eDeepvac会进行网络推理，推理结果赋值给self.config.output成员\u003c/td\u003e\n\u003ctd\u003e用户可以重新定义（如果需要的话）\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edoTest\u003c/td\u003e\n\u003ctd\u003e用户完全自定义的test逻辑，可以通过report.add(gt, pred)添加测试结果，生成报告\u003c/td\u003e\n\u003ctd\u003e看下面的测试逻辑\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典型的写法如下：\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"class MyTest(Deepvac):\n ...\n def doTest(self):\n ...\n\ntest = MyTest(deepvac_config)\ntest()\n#test(input_tensor)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyTest\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eDeepvac\u003c/span\u003e):\n ...\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003edoTest\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n ...\n\n\u003cspan class=\"pl-s1\"\u003etest\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMyTest\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edeepvac_config\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003etest\u003c/span\u003e()\n\u003cspan class=\"pl-c\"\u003e#test(input_tensor)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e当执行test()的时候，DeepVAC框架会按照如下的优先级进行测试：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e如果用户传递了参数，比如test(input_tensor)，则将针对该input_tensor进行doFeedData2Device + doForward，然后测试结束；\u003c/li\u003e\n\u003cli\u003e如果用户重写了doTest()函数，则将执行doTest()，然后测试结束；\u003c/li\u003e\n\u003cli\u003e如果用户配置了config.my_test.test_loader，则将迭代该loader，对每个sample进行doFeedData2Device + doForward，然后测试结束；\u003c/li\u003e\n\u003cli\u003e以上都不符合，报错退出。\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepVAC的社区产品\u003c/h1\u003e\u003ca id=\"user-content-deepvac的社区产品\" class=\"anchor\" aria-label=\"Permalink: DeepVAC的社区产品\" href=\"#deepvac的社区产品\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e产品名称\u003c/th\u003e\n\u003cth\u003e简介\u003c/th\u003e\n\u003cth\u003e当前版本\u003c/th\u003e\n\u003cth\u003e获取方式/部署形式\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepvac/deepvac\"\u003eDeepVAC\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e独树一帜的PyTorch工程规范\u003c/td\u003e\n\u003ctd\u003e0.6.0\u003c/td\u003e\n\u003ctd\u003epip install deepvac\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepvac/libdeepvac\"\u003elibdeepvac\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e独树一帜的PyTorch模型部署框架\u003c/td\u003e\n\u003ctd\u003e1.9.0\u003c/td\u003e\n\u003ctd\u003eSDK，下载 \u0026amp; 解压\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/DeepVAC/MLab#mlab-homepod\"\u003eMLab HomePod\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e迄今为止最先进的容器化PyTorch模型训练环境\u003c/td\u003e\n\u003ctd\u003e2.0\u003c/td\u003e\n\u003ctd\u003edocker run / k8s\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMLab RookPod\u003c/td\u003e\n\u003ctd\u003e迄今为止最先进的成本10万人民币以下的存储解决方案\u003c/td\u003e\n\u003ctd\u003eNA\u003c/td\u003e\n\u003ctd\u003e硬件规范 + k8s yaml\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/DeepVAC/pyRBAC\"\u003epyRBAC\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e基于Keycloak的RBAC python实现\u003c/td\u003e\n\u003ctd\u003eNA\u003c/td\u003e\n\u003ctd\u003epip install(敬请期待)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDeepVAC版PyTorch\u003c/td\u003e\n\u003ctd\u003e为MLab HomePod pro版本定制的PyTorch包\u003c/td\u003e\n\u003ctd\u003e1.9.0\u003c/td\u003e\n\u003ctd\u003econda install -c gemfield pytorch\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepvac/libdeepvac\"\u003eDeepVAC版LibTorch\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e为libdeepvac定制的LibTorch库\u003c/td\u003e\n\u003ctd\u003e1.9.0\u003c/td\u003e\n\u003ctd\u003e压缩包，下载 \u0026amp; 解压\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"DeepVAC","anchor":"deepvac","htmlText":"DeepVAC"},{"level":1,"text":"如何基于DeepVAC构建自己的PyTorch AI项目","anchor":"如何基于deepvac构建自己的pytorch-ai项目","htmlText":"如何基于DeepVAC构建自己的PyTorch AI项目"},{"level":2,"text":"1. 阅读DeepVAC标准","anchor":"1-阅读deepvac标准","htmlText":"1. 阅读DeepVAC标准"},{"level":2,"text":"2. 环境准备","anchor":"2-环境准备","htmlText":"2. 环境准备"},{"level":2,"text":"3. 安装deepvac库","anchor":"3-安装deepvac库","htmlText":"3. 安装deepvac库"},{"level":4,"text":"开发者模式","anchor":"开发者模式","htmlText":"开发者模式"},{"level":2,"text":"4. 创建自己的PyTorch项目","anchor":"4-创建自己的pytorch项目","htmlText":"4. 创建自己的PyTorch项目"},{"level":2,"text":"5. 编写配置文件","anchor":"5-编写配置文件","htmlText":"5. 编写配置文件"},{"level":2,"text":"6. 编写synthesis/synthesis.py（可选）","anchor":"6-编写synthesissynthesispy可选","htmlText":"6. 编写synthesis/synthesis.py（可选）"},{"level":2,"text":"7. 编写aug/aug.py（可选）","anchor":"7-编写augaugpy可选","htmlText":"7. 编写aug/aug.py（可选）"},{"level":2,"text":"8. 编写Dataset类","anchor":"8-编写dataset类","htmlText":"8. 编写Dataset类"},{"level":2,"text":"9. 编写训练和验证脚本","anchor":"9-编写训练和验证脚本","htmlText":"9. 编写训练和验证脚本"},{"level":2,"text":"10. 编写测试脚本","anchor":"10-编写测试脚本","htmlText":"10. 编写测试脚本"},{"level":1,"text":"DeepVAC的社区产品","anchor":"deepvac的社区产品","htmlText":"DeepVAC的社区产品"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FDeepVAC%2Fdeepvac"}},{"displayName":"LICENSE","repoName":"deepvac","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"GPL-3.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FDeepVAC%2Fdeepvac"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div 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0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">DeepVAC</h1><a id="user-content-deepvac" class="anchor" aria-label="Permalink: DeepVAC" href="#deepvac"><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">DeepVAC提供了基于PyTorch的AI项目的工程化规范。为了达到这一目标，DeepVAC包含了：</p> <ul dir="auto"> <li>软件工程规范：<a href="/DeepVAC/deepvac/blob/master/docs/arch.md">软件工程规范</a>；</li> <li>代码规范：<a href="/DeepVAC/deepvac/blob/master/docs/code_standard.md">代码规范</a>；</li> <li>deepvac库：<a href="/DeepVAC/deepvac/blob/master/docs/lib.md">deepvac库</a>。</li> </ul> <p dir="auto">诸多PyTorch AI项目的内在逻辑都大同小异，因此DeepVAC致力于把更通用的逻辑剥离出来，从而使得工程代码的准确性、易读性、可维护性上更具优势。</p> <p dir="auto">如果想使得AI项目符合DeepVAC规范，需要仔细阅读<a href="/DeepVAC/deepvac/blob/master/docs/deepvac_standard.md">DeepVAC标准</a>。 如果想了解deepvac库的设计，请阅读<a href="/DeepVAC/deepvac/blob/master/docs/design.md">deepvac库的设计</a>。</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">如何基于DeepVAC构建自己的PyTorch AI项目</h1><a id="user-content-如何基于deepvac构建自己的pytorch-ai项目" class="anchor" aria-label="Permalink: 如何基于DeepVAC构建自己的PyTorch AI项目" href="#如何基于deepvac构建自己的pytorch-ai项目"><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"><h2 tabindex="-1" class="heading-element" dir="auto">1. 阅读<a href="/DeepVAC/deepvac/blob/master/docs/deepvac_standard.md">DeepVAC标准</a></h2><a id="user-content-1-阅读deepvac标准" class="anchor" aria-label="Permalink: 1. 阅读DeepVAC标准" href="#1-阅读deepvac标准"><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">可以粗略阅读，建立起第一印象。</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2. 环境准备</h2><a id="user-content-2-环境准备" class="anchor" aria-label="Permalink: 2. 环境准备" href="#2-环境准备"><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">DeepVAC的依赖有：</p> <ul dir="auto"> <li>Python3。不支持Python2，其已被废弃；</li> <li>依赖包：torch, torchvision, tensorboard, scipy, numpy, cv2, Pillow；</li> </ul> <p dir="auto">这些依赖使用pip命令（或者git clone）自行安装，不再赘述。</p> <p dir="auto">对于普通用户来说，最方便高效的方式还是使用<a href="https://github.com/DeepVAC/MLab#mlab-homepod">MLab HomePod</a>作为DeepVAC的使用环境，这是一个预构建的Docker image，可以帮助用户省掉不必要的环境配置时间。 同时在MLab组织内部，我们也使用<a href="https://github.com/DeepVAC/MLab#mlab-homepod">MLab HomePod</a>进行日常的模型的训练任务。</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">3. 安装deepvac库</h2><a id="user-content-3-安装deepvac库" class="anchor" aria-label="Permalink: 3. 安装deepvac库" href="#3-安装deepvac库"><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">可以使用pip来进行安装：<br> <code>pip3 install deepvac</code><br> 或者<br> <code>python3 -m pip install deepvac</code></p> <p dir="auto">如果你需要使用deepvac在github上的最新代码，就需要使用如下的开发者模式：</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">开发者模式</h4><a id="user-content-开发者模式" class="anchor" aria-label="Permalink: 开发者模式" href="#开发者模式"><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>克隆该项目到本地：<code>git clone https://github.com/DeepVAC/deepvac </code></li> <li>在你的入口文件中添加：</li> </ul> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import sys #replace with your local deepvac directory sys.path.insert(0,'/home/gemfield/github/deepvac')"><pre><span class="pl-k">import</span> <span class="pl-s1">sys</span> <span class="pl-c">#replace with your local deepvac directory</span> <span class="pl-s1">sys</span>.<span class="pl-c1">path</span>.<span class="pl-c1">insert</span>(<span class="pl-c1">0</span>,<span class="pl-s">'/home/gemfield/github/deepvac'</span>)</pre></div> <p dir="auto">或者设置PYTHONPATH环境变量：</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export PYTHONPATH=/home/gemfield/github/deepvac"><pre><span class="pl-k">export</span> PYTHONPATH=/home/gemfield/github/deepvac</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">4. 创建自己的PyTorch项目</h2><a id="user-content-4-创建自己的pytorch项目" class="anchor" aria-label="Permalink: 4. 创建自己的PyTorch项目" href="#4-创建自己的pytorch项目"><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>初始化自己项目的git仓库；</li> <li>在仓库中创建第一个研究分支，比如分支名为 LTS_b1_aug9_movie_video_plate_130w；</li> <li>切换到上述的LTS_b1分支中，开始工作；</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">5. 编写配置文件</h2><a id="user-content-5-编写配置文件" class="anchor" aria-label="Permalink: 5. 编写配置文件" href="#5-编写配置文件"><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">配置文件的文件名均为 config.py，位于你项目的根目录。在代码开始处添加<code>from deepvac import new, AttrDict</code>； 所有用户的配置都存放在这个文件里。config模块提供了6个预定义的作用域：config.core,config.aug,config.cast,config.datasets,config.backbones,config.loss。使用方法如下：</p> <ul dir="auto"> <li>所有和trainer相关（包括train、val、test）的配置都定义在config.core.<my_train_class>中；</li> <li>所有和deepvac.aug中增强模块相关的配置都定义在config.aug.<my_aug_class>中；</li> <li>所有和模型转换相关的配置都定义在config.cast.<the_caster_class>中；</li> <li>所有和Datasets相关的配置都定义在config.datasets.<my_dataset_class>中；</li> <li>所有和loss相关的配置都定义在config.loss.<my_loss_class>中；</li> <li>用户可以开辟自己的作用域，比如config.my_stuff = AttrDict()，然后config.my_stuff.name = 'gemfield'；</li> <li>用户可以使用new()来初始化config实例，使用clone()来深拷贝config配置项。</li> </ul> <p dir="auto">更多配置：</p> <ul dir="auto"> <li>预训练模型加载；</li> <li>checkpoint加载；</li> <li>tensorboard使用；</li> <li>TorchScript使用；</li> <li>转换ONNX；</li> <li>转换NCNN；</li> <li>转换CoreML；</li> <li>转换TensorRT；</li> <li>转换TNN；</li> <li>转换MNN；</li> <li>开启量化；</li> <li>开启EMA；</li> <li>开启自动混合精度训练(AMP)；</li> </ul> <p dir="auto">以及关于配置文件的更详细解释，请阅读<a href="/DeepVAC/deepvac/blob/master/docs/config.md">config说明</a>.</p> <p dir="auto">项目根目录下的train.py中用如下方式引用config.py文件:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from config import config as deepvac_config from deepvac import DeepvacTrain class MyTrain(DeepvacTrain): ...... my_train = MyTrain(deepvac_config) my_train()"><pre><span class="pl-k">from</span> <span class="pl-s1">config</span> <span class="pl-k">import</span> <span class="pl-s1">config</span> <span class="pl-k">as</span> <span class="pl-s1">deepvac_config</span> <span class="pl-k">from</span> <span class="pl-s1">deepvac</span> <span class="pl-k">import</span> <span class="pl-v">DeepvacTrain</span> <span class="pl-k">class</span> <span class="pl-v">MyTrain</span>(<span class="pl-v">DeepvacTrain</span>): ...... <span class="pl-s1">my_train</span> <span class="pl-c1">=</span> <span class="pl-en">MyTrain</span>(<span class="pl-s1">deepvac_config</span>) <span class="pl-en">my_train</span>()</pre></div> <p dir="auto">项目根目录下的test.py中用如下方式引用config.py文件:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from config import config as deepvac_config from deepvac import Deepvac class MyTest(Deepvac) ...... my_test = MyTest(deepvac_config) my_test()"><pre><span class="pl-k">from</span> <span class="pl-s1">config</span> <span class="pl-k">import</span> <span class="pl-s1">config</span> <span class="pl-k">as</span> <span class="pl-s1">deepvac_config</span> <span class="pl-k">from</span> <span class="pl-s1">deepvac</span> <span class="pl-k">import</span> <span class="pl-v">Deepvac</span> <span class="pl-k">class</span> <span class="pl-v">MyTest</span>(<span class="pl-v">Deepvac</span>) ...... <span class="pl-s1">my_test</span> <span class="pl-c1">=</span> <span class="pl-v">MyTest</span>(<span class="pl-s1">deepvac_config</span>) <span class="pl-s1">my_test</span>()</pre></div> <p dir="auto">之后，train.py/test.py代码中通过如下方式来读写config.core中的配置项</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="print(self.config.log_dir) print(self.config.batch_size) ......"><pre><span class="pl-en">print</span>(<span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">log_dir</span>) <span class="pl-en">print</span>(<span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">batch_size</span>) ......</pre></div> <p dir="auto">此外，鉴于config的核心作用，deepvac还设计了如下的API来方便对config模块的使用：</p> <ul dir="auto"> <li>AttrDict</li> <li>new</li> <li>interpret</li> <li>fork</li> <li>clone</li> </ul> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from deepvac import AttrDict, new, interpret, fork"><pre><span class="pl-k">from</span> <span class="pl-s1">deepvac</span> <span class="pl-k">import</span> <span class="pl-v">AttrDict</span>, <span class="pl-s1">new</span>, <span class="pl-s1">interpret</span>, <span class="pl-s1">fork</span></pre></div> <p dir="auto">关于这些API的使用方法，请访问<a href="/DeepVAC/deepvac/blob/master/docs/design.md#config-module">config API 说明</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">6. 编写synthesis/synthesis.py（可选）</h2><a id="user-content-6-编写synthesissynthesispy可选" class="anchor" aria-label="Permalink: 6. 编写synthesis/synthesis.py（可选）" href="#6-编写synthesissynthesispy可选"><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">编写该文件，用于产生本项目的数据集，用于对本项目的数据集进行自动化检查和清洗。 这一步为可选，如果有需要的话，可以参考Deepvac组织下Synthesis2D项目的实现。</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">7. 编写aug/aug.py（可选）</h2><a id="user-content-7-编写augaugpy可选" class="anchor" aria-label="Permalink: 7. 编写aug/aug.py（可选）" href="#7-编写augaugpy可选"><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">编写该文件，用于实现数据增强策略。 deepvac.aug模块为数据增强设计了特有的语法，在两个层面实现了复用：aug 和 composer。比如说，我想复用添加随机斑点的SpeckleAug：</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from deepvac.aug.base_aug import SpeckleAug"><pre><span class="pl-k">from</span> <span class="pl-s1">deepvac</span>.<span class="pl-s1">aug</span>.<span class="pl-s1">base_aug</span> <span class="pl-k">import</span> <span class="pl-v">SpeckleAug</span></pre></div> <p dir="auto">这是对底层aug算子的复用。我们还可以直接复用别人写好的composer，并且是以直截了当的方式。比如deepvac.aug提供了一个用于人脸检测数据增强的RetinaAugComposer：</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from deepvac.aug import RetinaAugComposer"><pre><span class="pl-k">from</span> <span class="pl-s1">deepvac</span>.<span class="pl-s1">aug</span> <span class="pl-k">import</span> <span class="pl-v">RetinaAugComposer</span></pre></div> <p dir="auto">以上说的是直接复用，但项目中更多的是自定义扩展，而且大部分情况下也需要复用torchvision的transform的compose，又该怎么办呢？这里解释下，composer是deepvac.aug模块的概念，compose是torchvision transform模块的概念，之所以这么相似纯粹是因为巧合。</p> <p dir="auto">要扩展自己的composer也是很简单的，比如我可以自定义一个composer（我把它命名为GemfieldComposer），这个composer可以使用/复用以下增强逻辑：</p> <ul dir="auto"> <li>torchvision transform定义的compose；</li> <li>deepvac内置的aug算子；</li> <li>我自己写的aug算子。</li> </ul> <p dir="auto">更详细的步骤请访问：<a href="/DeepVAC/deepvac/blob/master/docs/design.md#aug">deepvac.aug模块使用</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">8. 编写Dataset类</h2><a id="user-content-8-编写dataset类" class="anchor" aria-label="Permalink: 8. 编写Dataset类" href="#8-编写dataset类"><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">代码编写在data/dataloader.py文件中。继承deepvac.datasets类体系，比如FileLineDataset类提供了对如下train.txt这种格式的封装：</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="#train.txt，第一列为图片路径，第二列为label img0/1.jpg 0 img0/2.jpg 0 ... img1/0.jpg 1 ... img2/0.jpg 2 ..."><pre><span class="pl-c"><span class="pl-c">#</span>train.txt，第一列为图片路径，第二列为label</span> img0/1.jpg 0 img0/2.jpg 0 ... img1/0.jpg 1 ... img2/0.jpg 2 ...</pre></div> <p dir="auto">有时第二列是字符串，并且想把FileLineDataset中使用Image读取图片对方式替换为cv2，那么可以通过如下的继承方式来重新实现：</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from deepvac.datasets import FileLineDataset class FileLineCvStrDataset(FileLineDataset): def _buildLabelFromLine(self, line): line = line.strip().split(" ") return [line[0], line[1]] def _buildSampleFromPath(self, abs_path): #we just set default loader with Pillow Image sample = cv2.imread(abs_path) sample = self.compose(sample) return sample"><pre><span class="pl-k">from</span> <span class="pl-s1">deepvac</span>.<span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-v">FileLineDataset</span> <span class="pl-k">class</span> <span class="pl-v">FileLineCvStrDataset</span>(<span class="pl-v">FileLineDataset</span>): <span class="pl-k">def</span> <span class="pl-en">_buildLabelFromLine</span>(<span class="pl-s1">self</span>, <span class="pl-s1">line</span>): <span class="pl-s1">line</span> <span class="pl-c1">=</span> <span class="pl-s1">line</span>.<span class="pl-c1">strip</span>().<span class="pl-c1">split</span>(<span class="pl-s">" "</span>) <span class="pl-k">return</span> [<span class="pl-s1">line</span>[<span class="pl-c1">0</span>], <span class="pl-s1">line</span>[<span class="pl-c1">1</span>]] <span class="pl-k">def</span> <span class="pl-en">_buildSampleFromPath</span>(<span class="pl-s1">self</span>, <span class="pl-s1">abs_path</span>): <span class="pl-c">#we just set default loader with Pillow Image</span> <span class="pl-s1">sample</span> <span class="pl-c1">=</span> <span class="pl-s1">cv2</span>.<span class="pl-c1">imread</span>(<span class="pl-s1">abs_path</span>) <span class="pl-s1">sample</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">compose</span>(<span class="pl-s1">sample</span>) <span class="pl-k">return</span> <span class="pl-s1">sample</span></pre></div> <p dir="auto">哦，FileLineCvStrDataset也已经是deepvac.datasets中提供的类了。</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">9. 编写训练和验证脚本</h2><a id="user-content-9-编写训练和验证脚本" class="anchor" aria-label="Permalink: 9. 编写训练和验证脚本" href="#9-编写训练和验证脚本"><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">在Deepvac规范中，train.py就代表了训练范式。模型训练的代码写在train.py文件中，继承DeepvacTrain类：</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from deepvac import DeepvacTrain class MyTrain(DeepvacTrain): pass"><pre><span class="pl-k">from</span> <span class="pl-s1">deepvac</span> <span class="pl-k">import</span> <span class="pl-v">DeepvacTrain</span> <span class="pl-k">class</span> <span class="pl-v">MyTrain</span>(<span class="pl-v">DeepvacTrain</span>): <span class="pl-k">pass</span></pre></div> <p dir="auto">继承DeepvacTrain的子类可能需要重新实现以下方法才能够开始训练：</p> <markdown-accessiblity-table><table> <thead> <tr> <th>类的方法（*号表示用户一般要重新实现）</th> <th>功能</th> <th>备注</th> </tr> </thead> <tbody> <tr> <td>preEpoch</td> <td>每轮Epoch之前的用户操作，DeepvacTrain啥也不做</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>preIter</td> <td>每个batch迭代之前的用户操作，DeepvacTrain啥也不做</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>postIter</td> <td>每个batch迭代之后的用户操作，DeepvacTrain啥也不做</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>postEpoch</td> <td>每轮Epoch之后的用户操作，DeepvacTrain啥也不做</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doFeedData2Device</td> <td>DeepvacTrain把来自dataloader的sample和target(标签)移动到device设备上</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doForward</td> <td>DeepvacTrain会进行网络推理，推理结果赋值给self.config.output成员</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doLoss</td> <td>DeepvacTrain会使用self.config.output和self.config.target进行计算得到此次迭代的loss</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doBackward</td> <td>网络反向传播过程，DeepvacTrain会调用self.config.loss.backward()进行BP</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doOptimize</td> <td>网络权重更新的过程，DeepvacTrain会调用self.config.optimizer.step()</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doSchedule</td> <td>更新学习率的过程，DeepvacTrain会调用self.config.scheduler.step()</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>* doValAcc</td> <td>在val模式下计算模型的acc，DeepvacTrain啥也不做</td> <td>用户一般要重新定义，写tensorboard的时候依赖于此</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">典型的写法如下：</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="class MyTrain(DeepvacTrain): ... #因为基类不能处理list类型的标签，重写该方法 def doFeedData2Device(self): self.config.target = [anno.to(self.config.device) for anno in self.config.target] self.config.sample = self.config.sample.to(self.config.device) #初始化config.core.acc def doValAcc(self): self.config.acc = your_acc LOG.logI('Test accuray: {:.4f}'.format(self.config.acc)) train = MyTrain(deepvac_config) train()"><pre><span class="pl-k">class</span> <span class="pl-v">MyTrain</span>(<span class="pl-v">DeepvacTrain</span>): ... <span class="pl-c">#因为基类不能处理list类型的标签，重写该方法</span> <span class="pl-k">def</span> <span class="pl-en">doFeedData2Device</span>(<span class="pl-s1">self</span>): <span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">target</span> <span class="pl-c1">=</span> [<span class="pl-s1">anno</span>.<span class="pl-c1">to</span>(<span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">device</span>) <span class="pl-k">for</span> <span class="pl-s1">anno</span> <span class="pl-c1">in</span> <span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">target</span>] <span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">sample</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">sample</span>.<span class="pl-c1">to</span>(<span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">device</span>) <span class="pl-c">#初始化config.core.acc</span> <span class="pl-k">def</span> <span class="pl-en">doValAcc</span>(<span class="pl-s1">self</span>): <span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">acc</span> <span class="pl-c1">=</span> <span class="pl-s1">your_acc</span> <span class="pl-c1">LOG</span>.<span class="pl-c1">logI</span>(<span class="pl-s">'Test accuray: {:.4f}'</span>.<span class="pl-c1">format</span>(<span class="pl-s1">self</span>.<span class="pl-c1">config</span>.<span class="pl-c1">acc</span>)) <span class="pl-s1">train</span> <span class="pl-c1">=</span> <span class="pl-en">MyTrain</span>(<span class="pl-s1">deepvac_config</span>) <span class="pl-en">train</span>()</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">10. 编写测试脚本</h2><a id="user-content-10-编写测试脚本" class="anchor" aria-label="Permalink: 10. 编写测试脚本" href="#10-编写测试脚本"><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">在Deepvac规范中，test.py就代表测试范式。测试代码写在test.py文件中，继承Deepvac类。</p> <p dir="auto">和train.py中的train/val的本质不同在于：</p> <ul dir="auto"> <li>舍弃train/val上下文；</li> <li>网络不再使用autograd上下文；</li> <li>不再进行loss、反向、优化等计算；</li> <li>使用Deepvac的*Report模块来进行准确度、速度方面的衡量；</li> </ul> <p dir="auto">继承Deepvac类的子类必须（重新）实现以下方法才能够开始测试：</p> <markdown-accessiblity-table><table> <thead> <tr> <th>类的方法（*号表示必需重新实现）</th> <th>功能</th> <th>备注</th> </tr> </thead> <tbody> <tr> <td>preIter</td> <td>每个batch迭代之前的用户操作，Deepvac啥也不做</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>postIter</td> <td>每个batch迭代之后的用户操作，Deepvac啥也不做</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doFeedData2Device</td> <td>Deepvac把来自dataloader的sample和target(标签)移动到device设备上</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doForward</td> <td>Deepvac会进行网络推理，推理结果赋值给self.config.output成员</td> <td>用户可以重新定义（如果需要的话）</td> </tr> <tr> <td>doTest</td> <td>用户完全自定义的test逻辑，可以通过report.add(gt, pred)添加测试结果，生成报告</td> <td>看下面的测试逻辑</td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">典型的写法如下：</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="class MyTest(Deepvac): ... def doTest(self): ... test = MyTest(deepvac_config) test() #test(input_tensor)"><pre><span class="pl-k">class</span> <span class="pl-v">MyTest</span>(<span class="pl-v">Deepvac</span>): ... <span class="pl-k">def</span> <span class="pl-en">doTest</span>(<span class="pl-s1">self</span>): ... <span class="pl-s1">test</span> <span class="pl-c1">=</span> <span class="pl-en">MyTest</span>(<span class="pl-s1">deepvac_config</span>) <span class="pl-en">test</span>() <span class="pl-c">#test(input_tensor)</span></pre></div> <p dir="auto">当执行test()的时候，DeepVAC框架会按照如下的优先级进行测试：</p> <ul dir="auto"> <li>如果用户传递了参数，比如test(input_tensor)，则将针对该input_tensor进行doFeedData2Device + doForward，然后测试结束；</li> <li>如果用户重写了doTest()函数，则将执行doTest()，然后测试结束；</li> <li>如果用户配置了config.my_test.test_loader，则将迭代该loader，对每个sample进行doFeedData2Device + doForward，然后测试结束；</li> <li>以上都不符合，报错退出。</li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">DeepVAC的社区产品</h1><a id="user-content-deepvac的社区产品" class="anchor" aria-label="Permalink: DeepVAC的社区产品" href="#deepvac的社区产品"><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> <markdown-accessiblity-table><table> <thead> <tr> <th>产品名称</th> <th>简介</th> <th>当前版本</th> <th>获取方式/部署形式</th> </tr> </thead> <tbody> <tr> <td><a href="https://github.com/deepvac/deepvac">DeepVAC</a></td> <td>独树一帜的PyTorch工程规范</td> <td>0.6.0</td> <td>pip install deepvac</td> </tr> <tr> <td><a href="https://github.com/deepvac/libdeepvac">libdeepvac</a></td> <td>独树一帜的PyTorch模型部署框架</td> <td>1.9.0</td> <td>SDK，下载 & 解压</td> </tr> <tr> <td><a href="https://github.com/DeepVAC/MLab#mlab-homepod">MLab HomePod</a></td> <td>迄今为止最先进的容器化PyTorch模型训练环境</td> <td>2.0</td> <td>docker run / k8s</td> </tr> <tr> <td>MLab RookPod</td> <td>迄今为止最先进的成本10万人民币以下的存储解决方案</td> <td>NA</td> <td>硬件规范 + k8s yaml</td> </tr> <tr> <td><a href="https://github.com/DeepVAC/pyRBAC">pyRBAC</a></td> <td>基于Keycloak的RBAC python实现</td> <td>NA</td> <td>pip install(敬请期待)</td> </tr> <tr> <td>DeepVAC版PyTorch</td> <td>为MLab HomePod pro版本定制的PyTorch包</td> <td>1.9.0</td> <td>conda install -c gemfield pytorch</td> </tr> <tr> <td><a href="https://github.com/deepvac/libdeepvac">DeepVAC版LibTorch</a></td> <td>为libdeepvac定制的LibTorch库</td> <td>1.9.0</td> <td>压缩包，下载 & 解压</td> </tr> </tbody> 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