2019-03-25 01:22:43 +08:00
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2019-03-25 01:22:43 +08:00
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2019-05-22 23:18:39 +08:00
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< div class = "section" id = "torchreid-optim" >
< span id = "id1" > < / span > < h1 > torchreid.optim< a class = "headerlink" href = "#torchreid-optim" title = "Permalink to this headline" > ¶< / a > < / h1 >
< div class = "section" id = "module-torchreid.optim.optimizer" >
< span id = "optimizer" > < / span > < h2 > Optimizer< a class = "headerlink" href = "#module-torchreid.optim.optimizer" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "function" >
< dt id = "torchreid.optim.optimizer.build_optimizer" >
< code class = "descclassname" > torchreid.optim.optimizer.< / code > < code class = "descname" > build_optimizer< / code > < span class = "sig-paren" > (< / span > < em > model< / em > , < em > optim='adam'< / em > , < em > lr=0.0003< / em > , < em > weight_decay=0.0005< / em > , < em > momentum=0.9< / em > , < em > sgd_dampening=0< / em > , < em > sgd_nesterov=False< / em > , < em > rmsprop_alpha=0.99< / em > , < em > adam_beta1=0.9< / em > , < em > adam_beta2=0.99< / em > , < em > staged_lr=False< / em > , < em > new_layers=''< / em > , < em > base_lr_mult=0.1< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/optim/optimizer.html#build_optimizer" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.optim.optimizer.build_optimizer" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > A function wrapper for building an optimizer.< / p >
< 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 > model< / strong > (< em > nn.Module< / em > ) – < / li >
< li > < strong > optim< / strong > (< em > str< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > lr< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > weight_decay< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > momentum< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > sgd_dampening< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > sgd_nesterov< / strong > (< em > bool< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > rmsprop_alpha< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > adam_beta1< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > adam_beta2< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< li > < strong > staged_lr< / strong > (< em > bool< / em > < em > , < / em > < em > optional< / em > ) –
layers are pretrained layers while new layers are randomly initialized, e.g. the
identity classification layer. Enabling < code class = "docutils literal notranslate" > < span class = "pre" > staged_lr< / span > < / code > can allow the base layers to
be trained with a smaller learning rate determined by < code class = "docutils literal notranslate" > < span class = "pre" > base_lr_mult< / span > < / code > , while the new
layers will take the < code class = "docutils literal notranslate" > < span class = "pre" > lr< / span > < / code > . Default is False.< / li >
< li > < strong > new_layers< / strong > (< em > str< / em > < em > or < / em > < em > list< / em > ) – < code class = "docutils literal notranslate" > < span class = "pre" > model< / span > < / code > . Default is empty.< / li >
< li > < strong > base_lr_mult< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< / ul >
< / 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 = "c1" > # A normal optimizer can be built by< / span >
< span class = "gp" > > > > < / span > < span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > build_optimizer< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > optim< / span > < span class = "o" > =< / span > < span class = "s1" > ' sgd' < / span > < span class = "p" > ,< / span > < span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "mf" > 0.01< / span > < span class = "p" > )< / span >
< span class = "gp" > > > > < / span > < span class = "c1" > # If you want to use a smaller learning rate for pretrained layers< / span >
< span class = "gp" > > > > < / span > < span class = "c1" > # and the attribute name for the randomly initialized layer is ' classifier' ,< / span >
< span class = "gp" > > > > < / span > < span class = "c1" > # you can do< / span >
< span class = "gp" > > > > < / span > < span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > build_optimizer< / span > < span class = "p" > (< / span >
< span class = "gp" > > > > < / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > optim< / span > < span class = "o" > =< / span > < span class = "s1" > ' sgd' < / span > < span class = "p" > ,< / span > < span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "n" > staged_lr< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span >
< span class = "gp" > > > > < / span > < span class = "n" > new_layers< / span > < span class = "o" > =< / span > < span class = "s1" > ' classifier' < / span > < span class = "p" > ,< / span > < span class = "n" > base_lr_mult< / span > < span class = "o" > =< / span > < span class = "mf" > 0.1< / span >
< span class = "gp" > > > > < / span > < span class = "p" > )< / span >
< span class = "gp" > > > > < / span > < span class = "c1" > # Now the `classifier` has learning rate 0.01 but the base layers< / span >
< span class = "gp" > > > > < / span > < span class = "c1" > # have learning rate 0.01 * 0.1.< / span >
< span class = "gp" > > > > < / span > < span class = "c1" > # new_layers can also take multiple attribute names. Say the new layers< / span >
< span class = "gp" > > > > < / span > < span class = "c1" > # are ' fc' and ' classifier' , you can do< / span >
< span class = "gp" > > > > < / span > < span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > build_optimizer< / span > < span class = "p" > (< / span >
< span class = "gp" > > > > < / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > optim< / span > < span class = "o" > =< / span > < span class = "s1" > ' sgd' < / span > < span class = "p" > ,< / span > < span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "n" > staged_lr< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span >
< span class = "gp" > > > > < / span > < span class = "n" > new_layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s1" > ' fc' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' classifier' < / span > < span class = "p" > ],< / span > < span class = "n" > base_lr_mult< / span > < span class = "o" > =< / span > < span class = "mf" > 0.1< / span >
< span class = "gp" > > > > < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< / dd >
< / dl >
< / dd > < / dl >
< / div >
< div class = "section" id = "module-torchreid.optim.lr_scheduler" >
< span id = "lr-scheduler" > < / span > < h2 > LR Scheduler< a class = "headerlink" href = "#module-torchreid.optim.lr_scheduler" title = "Permalink to this headline" > ¶< / a > < / h2 >
< dl class = "function" >
< dt id = "torchreid.optim.lr_scheduler.build_lr_scheduler" >
< code class = "descclassname" > torchreid.optim.lr_scheduler.< / code > < code class = "descname" > build_lr_scheduler< / code > < span class = "sig-paren" > (< / span > < em > optimizer< / em > , < em > lr_scheduler< / em > , < em > stepsize< / em > , < em > gamma=0.1< / em > < span class = "sig-paren" > )< / span > < a class = "reference internal" href = "../_modules/torchreid/optim/lr_scheduler.html#build_lr_scheduler" > < span class = "viewcode-link" > [source]< / span > < / a > < a class = "headerlink" href = "#torchreid.optim.lr_scheduler.build_lr_scheduler" title = "Permalink to this definition" > ¶< / a > < / dt >
< dd > < p > A function wrapper for building a learning rate scheduler.< / p >
< 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 > optimizer< / strong > (< em > Optimizer< / em > ) – < / li >
< li > < strong > lr_scheduler< / strong > (< em > str< / em > ) –
“single_step” and “multi_step”.< / li >
< li > < strong > stepsize< / strong > (< em > int< / em > < em > or < / em > < em > list< / em > ) – < code class = "docutils literal notranslate" > < span class = "pre" > lr_scheduler< / span > < / code > is
“single_step”, < code class = "docutils literal notranslate" > < span class = "pre" > stepsize< / span > < / code > should be an integer. When < code class = "docutils literal notranslate" > < span class = "pre" > lr_scheduler< / span > < / code > is
“multi_step”, < code class = "docutils literal notranslate" > < span class = "pre" > stepsize< / span > < / code > is a list.< / li >
< li > < strong > gamma< / strong > (< em > float< / em > < em > , < / em > < em > optional< / em > ) – < / li >
< / ul >
< / 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 = "c1" > # Decay learning rate by every 20 epochs.< / span >
< span class = "gp" > > > > < / span > < span class = "n" > scheduler< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > build_lr_scheduler< / span > < span class = "p" > (< / span >
< span class = "gp" > > > > < / span > < span class = "n" > optimizer< / span > < span class = "p" > ,< / span > < span class = "n" > lr_scheduler< / span > < span class = "o" > =< / span > < span class = "s1" > ' single_step' < / span > < span class = "p" > ,< / span > < span class = "n" > stepsize< / span > < span class = "o" > =< / span > < span class = "mi" > 20< / span >
< span class = "gp" > > > > < / span > < span class = "p" > )< / span >
< span class = "gp" > > > > < / span > < span class = "c1" > # Decay learning rate at 30, 50 and 55 epochs.< / span >
< span class = "gp" > > > > < / span > < span class = "n" > scheduler< / span > < span class = "o" > =< / span > < span class = "n" > torchreid< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > build_lr_scheduler< / span > < span class = "p" > (< / span >
< span class = "gp" > > > > < / span > < span class = "n" > optimizer< / span > < span class = "p" > ,< / span > < span class = "n" > lr_scheduler< / span > < span class = "o" > =< / span > < span class = "s1" > ' multi_step' < / span > < span class = "p" > ,< / span > < span class = "n" > stepsize< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 30< / span > < span class = "p" > ,< / span > < span class = "mi" > 50< / span > < span class = "p" > ,< / span > < span class = "mi" > 55< / span > < span class = "p" > ]< / span >
< span class = "gp" > > > > < / span > < span class = "p" > )< / span >
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< / div >
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< / dl >
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