2019-03-25 01:22:43 +08:00
<!DOCTYPE html>
<!-- [if IE 8]><html class="no - js lt - ie9" lang="en" > <![endif] -->
<!-- [if gt IE 8]><! --> < html class = "no-js" lang = "en" > <!-- <![endif] -->
< head >
< meta charset = "utf-8" >
< meta name = "viewport" content = "width=device-width, initial-scale=1.0" >
2019-05-22 23:18:39 +08:00
< title > torchreid.metrics.distance — torchreid 0.7.6 documentation< / title >
2019-03-25 01:22:43 +08:00
< script type = "text/javascript" src = "../../../_static/js/modernizr.min.js" > < / script >
< script type = "text/javascript" id = "documentation_options" data-url_root = "../../../" src = "../../../_static/documentation_options.js" > < / script >
< script type = "text/javascript" src = "../../../_static/jquery.js" > < / script >
< script type = "text/javascript" src = "../../../_static/underscore.js" > < / script >
< script type = "text/javascript" src = "../../../_static/doctools.js" > < / script >
< script type = "text/javascript" src = "../../../_static/language_data.js" > < / script >
< script async = "async" type = "text/javascript" src = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML" > < / script >
< script type = "text/javascript" src = "../../../_static/js/theme.js" > < / script >
< link rel = "stylesheet" href = "../../../_static/css/theme.css" type = "text/css" / >
< link rel = "stylesheet" href = "../../../_static/pygments.css" type = "text/css" / >
< link rel = "index" title = "Index" href = "../../../genindex.html" / >
< link rel = "search" title = "Search" href = "../../../search.html" / >
< / head >
< body class = "wy-body-for-nav" >
< div class = "wy-grid-for-nav" >
< nav data-toggle = "wy-nav-shift" class = "wy-nav-side" >
< div class = "wy-side-scroll" >
< div class = "wy-side-nav-search" >
< a href = "../../../index.html" class = "icon icon-home" > torchreid
< / a >
< div class = "version" >
2019-05-22 23:18:39 +08:00
0.7.6
2019-03-25 01:22:43 +08:00
< / div >
< div role = "search" >
< form id = "rtd-search-form" class = "wy-form" action = "../../../search.html" method = "get" >
< input type = "text" name = "q" placeholder = "Search docs" / >
< input type = "hidden" name = "check_keywords" value = "yes" / >
< input type = "hidden" name = "area" value = "default" / >
< / form >
< / div >
< / div >
< div class = "wy-menu wy-menu-vertical" data-spy = "affix" role = "navigation" aria-label = "main navigation" >
< ul >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../user_guide.html" > How-to< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../datasets.html" > Datasets< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../evaluation.html" > Evaluation< / a > < / li >
< / ul >
< p class = "caption" > < span class = "caption-text" > Package Reference< / span > < / p >
< ul >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../pkg/data.html" > torchreid.data< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../pkg/engine.html" > torchreid.engine< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../pkg/losses.html" > torchreid.losses< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../pkg/metrics.html" > torchreid.metrics< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../pkg/models.html" > torchreid.models< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../pkg/optim.html" > torchreid.optim< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../pkg/utils.html" > torchreid.utils< / a > < / li >
< / ul >
< p class = "caption" > < span class = "caption-text" > Resources< / span > < / p >
< ul >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../AWESOME_REID.html" > Awesome-ReID< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../../../MODEL_ZOO.html" > Model Zoo< / a > < / li >
< / ul >
< / div >
< / div >
< / nav >
< section data-toggle = "wy-nav-shift" class = "wy-nav-content-wrap" >
< nav class = "wy-nav-top" aria-label = "top navigation" >
< i data-toggle = "wy-nav-top" class = "fa fa-bars" > < / i >
< a href = "../../../index.html" > torchreid< / a >
< / nav >
< div class = "wy-nav-content" >
< div class = "rst-content" >
< div role = "navigation" aria-label = "breadcrumbs navigation" >
< ul class = "wy-breadcrumbs" >
< li > < a href = "../../../index.html" > Docs< / a > » < / li >
< li > < a href = "../../index.html" > Module code< / a > » < / li >
< li > torchreid.metrics.distance< / li >
< li class = "wy-breadcrumbs-aside" >
< / li >
< / ul >
< hr / >
< / div >
< div role = "main" class = "document" itemscope = "itemscope" itemtype = "http://schema.org/Article" >
< div itemprop = "articleBody" >
< h1 > Source code for torchreid.metrics.distance< / h1 > < div class = "highlight" > < pre >
< span > < / span > < span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > absolute_import< / span >
< span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > print_function< / span >
< span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > division< / span >
< span class = "kn" > import< / span > < span class = "nn" > numpy< / span > < span class = "k" > as< / span > < span class = "nn" > np< / span >
< span class = "kn" > import< / span > < span class = "nn" > torch< / span >
< span class = "kn" > from< / span > < span class = "nn" > torch.nn< / span > < span class = "k" > import< / span > < span class = "n" > functional< / span > < span class = "k" > as< / span > < span class = "n" > F< / span >
< div class = "viewcode-block" id = "compute_distance_matrix" > < a class = "viewcode-back" href = "../../../pkg/metrics.html#torchreid.metrics.distance.compute_distance_matrix" > [docs]< / a > < span class = "k" > def< / span > < span class = "nf" > compute_distance_matrix< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "n" > input2< / span > < span class = "p" > ,< / span > < span class = "n" > metric< / span > < span class = "o" > =< / span > < span class = "s1" > ' euclidean' < / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " A wrapper function for computing distance matrix.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > input1 (torch.Tensor): 2-D feature matrix.< / span >
< span class = "sd" > input2 (torch.Tensor): 2-D feature matrix.< / span >
< span class = "sd" > metric (str, optional): " euclidean" or " cosine" .< / span >
< span class = "sd" > Default is " euclidean" .< / span >
< span class = "sd" > Returns:< / span >
< span class = "sd" > torch.Tensor: distance matrix.< / span >
< span class = "sd" > Examples::< / span >
< span class = "sd" > > > > from torchreid import metrics< / span >
< span class = "sd" > > > > input1 = torch.rand(10, 2048)< / span >
< span class = "sd" > > > > input2 = torch.rand(100, 2048)< / span >
< span class = "sd" > > > > distmat = metrics.compute_distance_matrix(input1, input2)< / span >
< span class = "sd" > > > > distmat.size() # (10, 100)< / span >
< span class = "sd" > " " " < / span >
< span class = "c1" > # check input< / span >
< span class = "k" > assert< / span > < span class = "nb" > isinstance< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > Tensor< / span > < span class = "p" > )< / span >
< span class = "k" > assert< / span > < span class = "nb" > isinstance< / span > < span class = "p" > (< / span > < span class = "n" > input2< / span > < span class = "p" > ,< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > Tensor< / span > < span class = "p" > )< / span >
< span class = "k" > assert< / span > < span class = "n" > input1< / span > < span class = "o" > .< / span > < span class = "n" > dim< / span > < span class = "p" > ()< / span > < span class = "o" > ==< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "s1" > ' Expected 2-D tensor, but got < / span > < span class = "si" > {}< / span > < span class = "s1" > -D' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "o" > .< / span > < span class = "n" > dim< / span > < span class = "p" > ())< / span >
< span class = "k" > assert< / span > < span class = "n" > input2< / span > < span class = "o" > .< / span > < span class = "n" > dim< / span > < span class = "p" > ()< / span > < span class = "o" > ==< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "s1" > ' Expected 2-D tensor, but got < / span > < span class = "si" > {}< / span > < span class = "s1" > -D' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > input2< / span > < span class = "o" > .< / span > < span class = "n" > dim< / span > < span class = "p" > ())< / span >
< span class = "k" > assert< / span > < span class = "n" > input1< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span > < span class = "o" > ==< / span > < span class = "n" > input2< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > metric< / span > < span class = "o" > ==< / span > < span class = "s1" > ' euclidean' < / span > < span class = "p" > :< / span >
< span class = "n" > distmat< / span > < span class = "o" > =< / span > < span class = "n" > euclidean_squared_distance< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "n" > input2< / span > < span class = "p" > )< / span >
< span class = "k" > elif< / span > < span class = "n" > metric< / span > < span class = "o" > ==< / span > < span class = "s1" > ' cosine' < / span > < span class = "p" > :< / span >
< span class = "n" > distmat< / span > < span class = "o" > =< / span > < span class = "n" > cosine_distance< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "n" > input2< / span > < span class = "p" > )< / span >
< span class = "k" > else< / span > < span class = "p" > :< / span >
< span class = "k" > raise< / span > < span class = "ne" > ValueError< / span > < span class = "p" > (< / span >
< span class = "s1" > ' Unknown distance metric: < / span > < span class = "si" > {}< / span > < span class = "s1" > . ' < / span >
< span class = "s1" > ' Please choose either " euclidean" or " cosine" ' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > metric< / span > < span class = "p" > )< / span >
< span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > distmat< / span > < / div >
< div class = "viewcode-block" id = "euclidean_squared_distance" > < a class = "viewcode-back" href = "../../../pkg/metrics.html#torchreid.metrics.distance.euclidean_squared_distance" > [docs]< / a > < span class = "k" > def< / span > < span class = "nf" > euclidean_squared_distance< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "n" > input2< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " Computes euclidean squared distance.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > input1 (torch.Tensor): 2-D feature matrix.< / span >
< span class = "sd" > input2 (torch.Tensor): 2-D feature matrix.< / span >
< span class = "sd" > Returns:< / span >
< span class = "sd" > torch.Tensor: distance matrix.< / span >
< span class = "sd" > " " " < / span >
< span class = "n" > m< / span > < span class = "p" > ,< / span > < span class = "n" > n< / span > < span class = "o" > =< / span > < span class = "n" > input1< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ),< / span > < span class = "n" > input2< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span >
< span class = "n" > distmat< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > pow< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "mi" > 2< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > sum< / span > < span class = "p" > (< / span > < span class = "n" > dim< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > keepdim< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > expand< / span > < span class = "p" > (< / span > < span class = "n" > m< / span > < span class = "p" > ,< / span > < span class = "n" > n< / span > < span class = "p" > )< / span > < span class = "o" > +< / span > \
< span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > pow< / span > < span class = "p" > (< / span > < span class = "n" > input2< / span > < span class = "p" > ,< / span > < span class = "mi" > 2< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > sum< / span > < span class = "p" > (< / span > < span class = "n" > dim< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > keepdim< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > expand< / span > < span class = "p" > (< / span > < span class = "n" > n< / span > < span class = "p" > ,< / span > < span class = "n" > m< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > t< / span > < span class = "p" > ()< / span >
< span class = "n" > distmat< / span > < span class = "o" > .< / span > < span class = "n" > addmm_< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "n" > input2< / span > < span class = "o" > .< / span > < span class = "n" > t< / span > < span class = "p" > ())< / span >
< span class = "k" > return< / span > < span class = "n" > distmat< / span > < / div >
< div class = "viewcode-block" id = "cosine_distance" > < a class = "viewcode-back" href = "../../../pkg/metrics.html#torchreid.metrics.distance.cosine_distance" > [docs]< / a > < span class = "k" > def< / span > < span class = "nf" > cosine_distance< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "n" > input2< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " Computes cosine distance.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > input1 (torch.Tensor): 2-D feature matrix.< / span >
< span class = "sd" > input2 (torch.Tensor): 2-D feature matrix.< / span >
< span class = "sd" > Returns:< / span >
< span class = "sd" > torch.Tensor: distance matrix.< / span >
< span class = "sd" > " " " < / span >
< span class = "n" > input1_normed< / span > < span class = "o" > =< / span > < span class = "n" > F< / span > < span class = "o" > .< / span > < span class = "n" > normalize< / span > < span class = "p" > (< / span > < span class = "n" > input1< / span > < span class = "p" > ,< / span > < span class = "n" > p< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "n" > dim< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "n" > input2_normed< / span > < span class = "o" > =< / span > < span class = "n" > F< / span > < span class = "o" > .< / span > < span class = "n" > normalize< / span > < span class = "p" > (< / span > < span class = "n" > input2< / span > < span class = "p" > ,< / span > < span class = "n" > p< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "n" > dim< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "n" > distmat< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "o" > -< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > mm< / span > < span class = "p" > (< / span > < span class = "n" > input1_normed< / span > < span class = "p" > ,< / span > < span class = "n" > input2_normed< / span > < span class = "o" > .< / span > < span class = "n" > t< / span > < span class = "p" > ())< / span >
< span class = "k" > return< / span > < span class = "n" > distmat< / span > < / div >
< / pre > < / div >
< / div >
< / div >
< footer >
< hr / >
< div role = "contentinfo" >
< p >
© Copyright 2019, Kaiyang Zhou
< / p >
< / div >
Built with < a href = "http://sphinx-doc.org/" > Sphinx< / a > using a < a href = "https://github.com/rtfd/sphinx_rtd_theme" > theme< / a > provided by < a href = "https://readthedocs.org" > Read the Docs< / a > .
< / footer >
< / div >
< / div >
< / section >
< / div >
< script type = "text/javascript" >
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
< / script >
< / body >
< / html >
