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< h1 > Source code for torchreid.optim.optimizer< / 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" > import< / span > < span class = "nn" > warnings< / span >
< span class = "kn" > import< / span > < span class = "nn" > torch< / span >
< span class = "kn" > import< / span > < span class = "nn" > torch.nn< / span > < span class = "k" > as< / span > < span class = "nn" > nn< / span >
< span class = "n" > AVAI_OPTIMS< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s1" > ' adam' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' amsgrad' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' sgd' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' rmsprop' < / span > < span class = "p" > ]< / span >
< div class = "viewcode-block" id = "build_optimizer" > < a class = "viewcode-back" href = "../../../pkg/optim.html#torchreid.optim.optimizer.build_optimizer" > [docs]< / a > < span class = "k" > def< / span > < span class = "nf" > 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" > ' adam' < / span > < span class = "p" > ,< / span >
< span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "mf" > 0.0003< / span > < span class = "p" > ,< / span >
< span class = "n" > weight_decay< / span > < span class = "o" > =< / span > < span class = "mf" > 5e-04< / span > < span class = "p" > ,< / span >
< span class = "n" > momentum< / span > < span class = "o" > =< / span > < span class = "mf" > 0.9< / span > < span class = "p" > ,< / span >
< span class = "n" > sgd_dampening< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span >
< span class = "n" > sgd_nesterov< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > rmsprop_alpha< / span > < span class = "o" > =< / span > < span class = "mf" > 0.99< / span > < span class = "p" > ,< / span >
< span class = "n" > adam_beta1< / span > < span class = "o" > =< / span > < span class = "mf" > 0.9< / span > < span class = "p" > ,< / span >
< span class = "n" > adam_beta2< / span > < span class = "o" > =< / span > < span class = "mf" > 0.99< / span > < span class = "p" > ,< / span >
< span class = "n" > staged_lr< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > new_layers< / span > < span class = "o" > =< / span > < span class = "s1" > ' ' < / span > < span class = "p" > ,< / span >
< span class = "n" > base_lr_mult< / span > < span class = "o" > =< / span > < span class = "mf" > 0.1< / span >
< span class = "p" > ):< / span >
< span class = "sd" > " " " A function wrapper for building an optimizer.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > model (nn.Module): model.< / span >
< span class = "sd" > optim (str, optional): optimizer. Default is " adam" .< / span >
< span class = "sd" > lr (float, optional): learning rate. Default is 0.0003.< / span >
< span class = "sd" > weight_decay (float, optional): weight decay (L2 penalty). Default is 5e-04.< / span >
< span class = "sd" > momentum (float, optional): momentum factor in sgd. Default is 0.9,< / span >
< span class = "sd" > sgd_dampening (float, optional): dampening for momentum. Default is 0.< / span >
< span class = "sd" > sgd_nesterov (bool, optional): enables Nesterov momentum. Default is False.< / span >
< span class = "sd" > rmsprop_alpha (float, optional): smoothing constant for rmsprop. Default is 0.99.< / span >
< span class = "sd" > adam_beta1 (float, optional): beta-1 value in adam. Default is 0.9.< / span >
< span class = "sd" > adam_beta2 (float, optional): beta-2 value in adam. Default is 0.99,< / span >
< span class = "sd" > staged_lr (bool, optional): uses different learning rates for base and new layers. Base< / span >
< span class = "sd" > layers are pretrained layers while new layers are randomly initialized, e.g. the< / span >
< span class = "sd" > identity classification layer. Enabling ``staged_lr`` can allow the base layers to< / span >
< span class = "sd" > be trained with a smaller learning rate determined by ``base_lr_mult``, while the new< / span >
< span class = "sd" > layers will take the ``lr``. Default is False.< / span >
< span class = "sd" > new_layers (str or list): attribute names in ``model``. Default is empty.< / span >
< span class = "sd" > base_lr_mult (float, optional): learning rate multiplier for base layers. Default is 0.1.< / span >
< span class = "sd" > Examples::< / span >
< span class = "sd" > > > > # A normal optimizer can be built by< / span >
< span class = "sd" > > > > optimizer = torchreid.optim.build_optimizer(model, optim=' sgd' , lr=0.01)< / span >
< span class = "sd" > > > > # If you want to use a smaller learning rate for pretrained layers< / span >
< span class = "sd" > > > > # and the attribute name for the randomly initialized layer is ' classifier' ,< / span >
< span class = "sd" > > > > # you can do< / span >
< span class = "sd" > > > > optimizer = torchreid.optim.build_optimizer(< / span >
< span class = "sd" > > > > model, optim=' sgd' , lr=0.01, staged_lr=True,< / span >
< span class = "sd" > > > > new_layers=' classifier' , base_lr_mult=0.1< / span >
< span class = "sd" > > > > )< / span >
< span class = "sd" > > > > # Now the `classifier` has learning rate 0.01 but the base layers< / span >
< span class = "sd" > > > > # have learning rate 0.01 * 0.1.< / span >
< span class = "sd" > > > > # new_layers can also take multiple attribute names. Say the new layers< / span >
< span class = "sd" > > > > # are ' fc' and ' classifier' , you can do< / span >
< span class = "sd" > > > > optimizer = torchreid.optim.build_optimizer(< / span >
< span class = "sd" > > > > model, optim=' sgd' , lr=0.01, staged_lr=True,< / span >
< span class = "sd" > > > > new_layers=[' fc' , ' classifier' ], base_lr_mult=0.1< / span >
< span class = "sd" > > > > )< / span >
< span class = "sd" > " " " < / span >
< span class = "k" > if< / span > < span class = "n" > optim< / span > < span class = "ow" > not< / span > < span class = "ow" > in< / span > < span class = "n" > AVAI_OPTIMS< / span > < span class = "p" > :< / span >
< span class = "k" > raise< / span > < span class = "ne" > ValueError< / span > < span class = "p" > (< / span > < span class = "s1" > ' Unsupported optim: < / span > < span class = "si" > {}< / span > < span class = "s1" > . Must be one of < / span > < span class = "si" > {}< / span > < span class = "s1" > ' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > optim< / span > < span class = "p" > ,< / span > < span class = "n" > AVAI_OPTIMS< / span > < span class = "p" > ))< / span >
< span class = "k" > if< / span > < span class = "ow" > not< / span > < span class = "nb" > isinstance< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Module< / span > < span class = "p" > ):< / span >
< span class = "k" > raise< / span > < span class = "ne" > TypeError< / span > < span class = "p" > (< / span > < span class = "s1" > ' model given to build_optimizer must be an instance of nn.Module' < / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > staged_lr< / span > < span class = "p" > :< / span >
< span class = "k" > if< / span > < span class = "nb" > isinstance< / span > < span class = "p" > (< / span > < span class = "n" > new_layers< / span > < span class = "p" > ,< / span > < span class = "nb" > str< / span > < span class = "p" > ):< / span >
< span class = "k" > if< / span > < span class = "n" > new_layers< / span > < span class = "ow" > is< / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
< span class = "n" > warnings< / span > < span class = "o" > .< / span > < span class = "n" > warn< / span > < span class = "p" > (< / span > < span class = "s1" > ' new_layers is empty, therefore, staged_lr is useless' < / span > < span class = "p" > )< / span >
< span class = "n" > new_layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "n" > new_layers< / span > < span class = "p" > ]< / span >
< span class = "k" > if< / span > < span class = "nb" > isinstance< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > DataParallel< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > module< / span >
< span class = "n" > base_params< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
< span class = "n" > base_layers< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
< span class = "n" > new_params< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
< span class = "k" > for< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > module< / span > < span class = "ow" > in< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > named_children< / span > < span class = "p" > ():< / span >
< span class = "k" > if< / span > < span class = "n" > name< / span > < span class = "ow" > in< / span > < span class = "n" > new_layers< / span > < span class = "p" > :< / span >
< span class = "n" > new_params< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > p< / span > < span class = "k" > for< / span > < span class = "n" > p< / span > < span class = "ow" > in< / span > < span class = "n" > module< / span > < span class = "o" > .< / span > < span class = "n" > parameters< / span > < span class = "p" > ()]< / span >
< span class = "k" > else< / span > < span class = "p" > :< / span >
< span class = "n" > base_params< / span > < span class = "o" > +=< / span > < span class = "p" > [< / span > < span class = "n" > p< / span > < span class = "k" > for< / span > < span class = "n" > p< / span > < span class = "ow" > in< / span > < span class = "n" > module< / span > < span class = "o" > .< / span > < span class = "n" > parameters< / span > < span class = "p" > ()]< / span >
< span class = "n" > base_layers< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > (< / span > < span class = "n" > name< / span > < span class = "p" > )< / span >
< span class = "n" > param_groups< / span > < span class = "o" > =< / span > < span class = "p" > [< / span >
< span class = "p" > {< / span > < span class = "s1" > ' params' < / span > < span class = "p" > :< / span > < span class = "n" > base_params< / span > < span class = "p" > ,< / span > < span class = "s1" > ' lr' < / span > < span class = "p" > :< / span > < span class = "n" > lr< / span > < span class = "o" > *< / span > < span class = "n" > base_lr_mult< / span > < span class = "p" > },< / span >
< span class = "p" > {< / span > < span class = "s1" > ' params' < / span > < span class = "p" > :< / span > < span class = "n" > new_params< / span > < span class = "p" > },< / span >
< span class = "p" > ]< / span >
< span class = "k" > else< / span > < span class = "p" > :< / span >
< span class = "n" > param_groups< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > parameters< / span > < span class = "p" > ()< / span >
< span class = "k" > if< / span > < span class = "n" > optim< / span > < span class = "o" > ==< / span > < span class = "s1" > ' adam' < / span > < span class = "p" > :< / span >
< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > Adam< / span > < span class = "p" > (< / span >
< span class = "n" > param_groups< / span > < span class = "p" > ,< / span >
< span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "n" > lr< / span > < span class = "p" > ,< / span >
< span class = "n" > weight_decay< / span > < span class = "o" > =< / span > < span class = "n" > weight_decay< / span > < span class = "p" > ,< / span >
< span class = "n" > betas< / span > < span class = "o" > =< / span > < span class = "p" > (< / span > < span class = "n" > adam_beta1< / span > < span class = "p" > ,< / span > < span class = "n" > adam_beta2< / span > < span class = "p" > ),< / span >
< span class = "p" > )< / span >
< span class = "k" > elif< / span > < span class = "n" > optim< / span > < span class = "o" > ==< / span > < span class = "s1" > ' amsgrad' < / span > < span class = "p" > :< / span >
< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > Adam< / span > < span class = "p" > (< / span >
< span class = "n" > param_groups< / span > < span class = "p" > ,< / span >
< span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "n" > lr< / span > < span class = "p" > ,< / span >
< span class = "n" > weight_decay< / span > < span class = "o" > =< / span > < span class = "n" > weight_decay< / span > < span class = "p" > ,< / span >
< span class = "n" > betas< / span > < span class = "o" > =< / span > < span class = "p" > (< / span > < span class = "n" > adam_beta1< / span > < span class = "p" > ,< / span > < span class = "n" > adam_beta2< / span > < span class = "p" > ),< / span >
< span class = "n" > amsgrad< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span >
< span class = "p" > )< / span >
< span class = "k" > elif< / span > < span class = "n" > optim< / span > < span class = "o" > ==< / span > < span class = "s1" > ' sgd' < / span > < span class = "p" > :< / span >
< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > SGD< / span > < span class = "p" > (< / span >
< span class = "n" > param_groups< / span > < span class = "p" > ,< / span >
< span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "n" > lr< / span > < span class = "p" > ,< / span >
< span class = "n" > momentum< / span > < span class = "o" > =< / span > < span class = "n" > momentum< / span > < span class = "p" > ,< / span >
< span class = "n" > weight_decay< / span > < span class = "o" > =< / span > < span class = "n" > weight_decay< / span > < span class = "p" > ,< / span >
< span class = "n" > dampening< / span > < span class = "o" > =< / span > < span class = "n" > sgd_dampening< / span > < span class = "p" > ,< / span >
< span class = "n" > nesterov< / span > < span class = "o" > =< / span > < span class = "n" > sgd_nesterov< / span > < span class = "p" > ,< / span >
< span class = "p" > )< / span >
< span class = "k" > elif< / span > < span class = "n" > optim< / span > < span class = "o" > ==< / span > < span class = "s1" > ' rmsprop' < / span > < span class = "p" > :< / span >
< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > RMSprop< / span > < span class = "p" > (< / span >
< span class = "n" > param_groups< / span > < span class = "p" > ,< / span >
< span class = "n" > lr< / span > < span class = "o" > =< / span > < span class = "n" > lr< / span > < span class = "p" > ,< / span >
< span class = "n" > momentum< / span > < span class = "o" > =< / span > < span class = "n" > momentum< / span > < span class = "p" > ,< / span >
< span class = "n" > weight_decay< / span > < span class = "o" > =< / span > < span class = "n" > weight_decay< / span > < span class = "p" > ,< / span >
< span class = "n" > alpha< / span > < span class = "o" > =< / span > < span class = "n" > rmsprop_alpha< / span > < span class = "p" > ,< / span >
< span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > optimizer< / span > < / div >
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