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< span id = "id1" > < / span > < h1 > torchreid.metrics< a class = "headerlink" href = "#torchreid-metrics" title = "Permalink to this headline" > ¶< / a > < / h1 >
< div class = "section" id = "module-torchreid.metrics.distance" >
< span id = "distance" > < / span > < h2 > Distance< a class = "headerlink" href = "#module-torchreid.metrics.distance" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "function" >
< dt id = "torchreid.metrics.distance.compute_distance_matrix" >
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< code class = "descclassname" > torchreid.metrics.distance.< / code > < code class = "descname" > compute_distance_matrix< / code > < span class = "sig-paren" > (< / span > < em > input1< / em > , < em > input2< / em > , < em > metric='euclidean'< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/metrics/distance.html#compute_distance_matrix" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.metrics.distance.compute_distance_matrix" title = "Permalink to this definition" > ¶< / a > < / dt >
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< dd > < p > A wrapper function for computing distance matrix.< / p >
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< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > input1< / strong > (< em > torch.Tensor< / em > ) – < / li >
< li > < strong > input2< / strong > (< em > torch.Tensor< / em > ) – < / li >
< li > < strong > metric< / strong > (< em > str< / em > < em > , < / em > < em > optional< / em > ) –
Default is “euclidean”.< / li >
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< / ul >
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< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first" > distance matrix.< / p >
< / td >
< / tr >
< tr class = "field-odd field" > < th class = "field-name" > Return type:< / th > < td class = "field-body" > < p class = "first last" > torch.Tensor< / p >
< / td >
< / tr >
< / tbody >
< / table >
< dl class = "docutils" >
< dt > Examples::< / dt >
< dd > < div class = "first last highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "gp" > > > > < / span > < span class = "kn" > from< / span > < span class = "nn" > torchreid< / span > < span class = "k" > import< / span > < span class = "n" > metrics< / span >
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< span class = "gp" > > > > < / span > < span class = "n" > input1< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > rand< / span > < span class = "p" > (< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span > < span class = "mi" > 2048< / span > < span class = "p" > )< / span >
< span class = "gp" > > > > < / span > < span class = "n" > input2< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > rand< / span > < span class = "p" > (< / span > < span class = "mi" > 100< / span > < span class = "p" > ,< / span > < span class = "mi" > 2048< / span > < span class = "p" > )< / span >
< span class = "gp" > > > > < / span > < span class = "n" > distmat< / span > < span class = "o" > =< / span > < span class = "n" > metrics< / span > < span class = "o" > .< / span > < span class = "n" > 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 = "gp" > > > > < / span > < span class = "n" > distmat< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()< / span > < span class = "c1" > # (10, 100)< / span >
< / pre > < / div >
< / div >
< / dd >
< / dl >
< / dd > < / dl >
< dl class = "function" >
< dt id = "torchreid.metrics.distance.cosine_distance" >
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< code class = "descclassname" > torchreid.metrics.distance.< / code > < code class = "descname" > cosine_distance< / code > < span class = "sig-paren" > (< / span > < em > input1< / em > , < em > input2< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/metrics/distance.html#cosine_distance" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.metrics.distance.cosine_distance" title = "Permalink to this definition" > ¶< / a > < / dt >
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< dd > < p > Computes cosine distance.< / p >
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< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > input1< / strong > (< em > torch.Tensor< / em > ) – < / li >
< li > < strong > input2< / strong > (< em > torch.Tensor< / em > ) – < / li >
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< / ul >
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< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first" > distance matrix.< / p >
< / td >
< / tr >
< tr class = "field-odd field" > < th class = "field-name" > Return type:< / th > < td class = "field-body" > < p class = "first last" > torch.Tensor< / p >
< / td >
< / tr >
< / tbody >
< / table >
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< / dd > < / dl >
< dl class = "function" >
< dt id = "torchreid.metrics.distance.euclidean_squared_distance" >
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< code class = "descclassname" > torchreid.metrics.distance.< / code > < code class = "descname" > euclidean_squared_distance< / code > < span class = "sig-paren" > (< / span > < em > input1< / em > , < em > input2< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/metrics/distance.html#euclidean_squared_distance" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.metrics.distance.euclidean_squared_distance" title = "Permalink to this definition" > ¶< / a > < / dt >
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< dd > < p > Computes euclidean squared distance.< / p >
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< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > input1< / strong > (< em > torch.Tensor< / em > ) – < / li >
< li > < strong > input2< / strong > (< em > torch.Tensor< / em > ) – < / li >
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< / ul >
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< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first" > distance matrix.< / p >
< / td >
< / tr >
< tr class = "field-odd field" > < th class = "field-name" > Return type:< / th > < td class = "field-body" > < p class = "first last" > torch.Tensor< / p >
< / td >
< / tr >
< / tbody >
< / table >
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< / dd > < / dl >
< / div >
< div class = "section" id = "module-torchreid.metrics.accuracy" >
< span id = "accuracy" > < / span > < h2 > Accuracy< a class = "headerlink" href = "#module-torchreid.metrics.accuracy" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "function" >
< dt id = "torchreid.metrics.accuracy.accuracy" >
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< code class = "descclassname" > torchreid.metrics.accuracy.< / code > < code class = "descname" > accuracy< / code > < span class = "sig-paren" > (< / span > < em > output< / em > , < em > target< / em > , < em > topk=(1< / em > , < em > )< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/metrics/accuracy.html#accuracy" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.metrics.accuracy.accuracy" title = "Permalink to this definition" > ¶< / a > < / dt >
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< dd > < p > Computes the accuracy over the k top predictions for
the specified values of k.< / p >
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< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first simple" >
< li > < strong > output< / strong > (< em > torch.Tensor< / em > ) – < / li >
< li > < strong > target< / strong > (< em > torch.LongTensor< / em > ) – < / li >
< li > < strong > topk< / strong > (< em > tuple< / em > < em > , < / em > < em > optional< / em > ) –
topk=(1, 5) means accuracy at top-1 and top-5 will be computed.< / li >
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< / ul >
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< / td >
< / tr >
< tr class = "field-even field" > < th class = "field-name" > Returns:< / th > < td class = "field-body" > < p class = "first" > accuracy at top-k.< / p >
< / td >
< / tr >
< tr class = "field-odd field" > < th class = "field-name" > Return type:< / th > < td class = "field-body" > < p class = "first last" > list< / p >
< / td >
< / tr >
< / tbody >
< / table >
< dl class = "docutils" >
< dt > Examples::< / dt >
< dd > < div class = "first last highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "gp" > > > > < / span > < span class = "kn" > from< / span > < span class = "nn" > torchreid< / span > < span class = "k" > import< / span > < span class = "n" > metrics< / span >
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< span class = "gp" > > > > < / span > < span class = "n" > metrics< / span > < span class = "o" > .< / span > < span class = "n" > accuracy< / span > < span class = "p" > (< / span > < span class = "n" > output< / span > < span class = "p" > ,< / span > < span class = "n" > target< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< / dd >
< / dl >
< / dd > < / dl >
< / div >
< div class = "section" id = "module-torchreid.metrics.rank" >
< span id = "rank" > < / span > < h2 > Rank< a class = "headerlink" href = "#module-torchreid.metrics.rank" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "function" >
< dt id = "torchreid.metrics.rank.evaluate_rank" >
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< code class = "descclassname" > torchreid.metrics.rank.< / code > < code class = "descname" > evaluate_rank< / code > < span class = "sig-paren" > (< / span > < em > distmat< / em > , < em > q_pids< / em > , < em > g_pids< / em > , < em > q_camids< / em > , < em > g_camids< / em > , < em > max_rank=50< / em > , < em > use_metric_cuhk03=False< / em > , < em > use_cython=True< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/metrics/rank.html#evaluate_rank" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.metrics.rank.evaluate_rank" title = "Permalink to this definition" > ¶< / a > < / dt >
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< dd > < p > Evaluates CMC rank.< / p >
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< table class = "docutils field-list" frame = "void" rules = "none" >
< col class = "field-name" / >
< col class = "field-body" / >
< tbody valign = "top" >
< tr class = "field-odd field" > < th class = "field-name" > Parameters:< / th > < td class = "field-body" > < ul class = "first last simple" >
< li > < strong > distmat< / strong > (< em > numpy.ndarray< / em > ) – < / li >
< li > < strong > q_pids< / strong > (< em > numpy.ndarray< / em > ) –
of each query instance.< / li >
< li > < strong > g_pids< / strong > (< em > numpy.ndarray< / em > ) –
of each gallery instance.< / li >
< li > < strong > q_camids< / strong > (< em > numpy.ndarray< / em > ) –
which each query instance is captured.< / li >
< li > < strong > g_camids< / strong > (< em > numpy.ndarray< / em > ) –
which each gallery instance is captured.< / li >
< li > < strong > max_rank< / strong > (< em > int< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > use_metric_cuhk03< / strong > (< em > bool< / em > < em > , < / em > < em > optional< / em > ) –
Default is False. This should be enabled when using cuhk03 classic split.< / li >
< li > < strong > use_cython< / strong > (< em > bool< / em > < em > , < / em > < em > optional< / em > ) –
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This is highly recommended as the cython code can speed up the cmc computation
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by more than 10x. This requires Cython to be installed.< / li >
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< / ul >
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< / tr >
< / tbody >
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