2019-03-25 01:22:43 +08:00
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< h1 > Source code for torchreid.models.__init__< / 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" > import< / span > < span class = "nn" > torch< / span >
< span class = "kn" > from< / span > < span class = "nn" > .resnet< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .resnetmid< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .senet< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .densenet< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .inceptionresnetv2< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .inceptionv4< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .xception< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .nasnet< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .mobilenetv2< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .shufflenet< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .squeezenet< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
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< span class = "kn" > from< / span > < span class = "nn" > .shufflenetv2< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
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< span class = "kn" > from< / span > < span class = "nn" > .mudeep< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .hacnn< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .pcb< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
< span class = "kn" > from< / span > < span class = "nn" > .mlfn< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
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< span class = "kn" > from< / span > < span class = "nn" > .osnet< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
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< span class = "kn" > from< / span > < span class = "nn" > .osnet_ain< / span > < span class = "k" > import< / span > < span class = "o" > *< / span >
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< span class = "n" > __model_factory< / span > < span class = "o" > =< / span > < span class = "p" > {< / span >
< span class = "c1" > # image classification models< / span >
< span class = "s1" > ' resnet18' < / span > < span class = "p" > :< / span > < span class = "n" > resnet18< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' resnet34' < / span > < span class = "p" > :< / span > < span class = "n" > resnet34< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' resnet50' < / span > < span class = "p" > :< / span > < span class = "n" > resnet50< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' resnet101' < / span > < span class = "p" > :< / span > < span class = "n" > resnet101< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' resnet152' < / span > < span class = "p" > :< / span > < span class = "n" > resnet152< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' resnext50_32x4d' < / span > < span class = "p" > :< / span > < span class = "n" > resnext50_32x4d< / span > < span class = "p" > ,< / span >
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< span class = "s1" > ' resnext101_32x8d' < / span > < span class = "p" > :< / span > < span class = "n" > resnext101_32x8d< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' resnet50_fc512' < / span > < span class = "p" > :< / span > < span class = "n" > resnet50_fc512< / span > < span class = "p" > ,< / span >
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< span class = "s1" > ' se_resnet50' < / span > < span class = "p" > :< / span > < span class = "n" > se_resnet50< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnet50_fc512' < / span > < span class = "p" > :< / span > < span class = "n" > se_resnet50_fc512< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnet101' < / span > < span class = "p" > :< / span > < span class = "n" > se_resnet101< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnext50_32x4d' < / span > < span class = "p" > :< / span > < span class = "n" > se_resnext50_32x4d< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnext101_32x4d' < / span > < span class = "p" > :< / span > < span class = "n" > se_resnext101_32x4d< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' densenet121' < / span > < span class = "p" > :< / span > < span class = "n" > densenet121< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' densenet169' < / span > < span class = "p" > :< / span > < span class = "n" > densenet169< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' densenet201' < / span > < span class = "p" > :< / span > < span class = "n" > densenet201< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' densenet161' < / span > < span class = "p" > :< / span > < span class = "n" > densenet161< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' densenet121_fc512' < / span > < span class = "p" > :< / span > < span class = "n" > densenet121_fc512< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' inceptionresnetv2' < / span > < span class = "p" > :< / span > < span class = "n" > inceptionresnetv2< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' inceptionv4' < / span > < span class = "p" > :< / span > < span class = "n" > inceptionv4< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' xception' < / span > < span class = "p" > :< / span > < span class = "n" > xception< / span > < span class = "p" > ,< / span >
< span class = "c1" > # lightweight models< / span >
< span class = "s1" > ' nasnsetmobile' < / span > < span class = "p" > :< / span > < span class = "n" > nasnetamobile< / span > < span class = "p" > ,< / span >
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< span class = "s1" > ' mobilenetv2_x1_0' < / span > < span class = "p" > :< / span > < span class = "n" > mobilenetv2_x1_0< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' mobilenetv2_x1_4' < / span > < span class = "p" > :< / span > < span class = "n" > mobilenetv2_x1_4< / span > < span class = "p" > ,< / span >
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< span class = "s1" > ' shufflenet' < / span > < span class = "p" > :< / span > < span class = "n" > shufflenet< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' squeezenet1_0' < / span > < span class = "p" > :< / span > < span class = "n" > squeezenet1_0< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' squeezenet1_0_fc512' < / span > < span class = "p" > :< / span > < span class = "n" > squeezenet1_0_fc512< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' squeezenet1_1' < / span > < span class = "p" > :< / span > < span class = "n" > squeezenet1_1< / span > < span class = "p" > ,< / span >
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< span class = "s1" > ' shufflenet_v2_x0_5' < / span > < span class = "p" > :< / span > < span class = "n" > shufflenet_v2_x0_5< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' shufflenet_v2_x1_0' < / span > < span class = "p" > :< / span > < span class = "n" > shufflenet_v2_x1_0< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' shufflenet_v2_x1_5' < / span > < span class = "p" > :< / span > < span class = "n" > shufflenet_v2_x1_5< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' shufflenet_v2_x2_0' < / span > < span class = "p" > :< / span > < span class = "n" > shufflenet_v2_x2_0< / span > < span class = "p" > ,< / span >
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< span class = "c1" > # reid-specific models< / span >
< span class = "s1" > ' mudeep' < / span > < span class = "p" > :< / span > < span class = "n" > MuDeep< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' resnet50mid' < / span > < span class = "p" > :< / span > < span class = "n" > resnet50mid< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' hacnn' < / span > < span class = "p" > :< / span > < span class = "n" > HACNN< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' pcb_p6' < / span > < span class = "p" > :< / span > < span class = "n" > pcb_p6< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' pcb_p4' < / span > < span class = "p" > :< / span > < span class = "n" > pcb_p4< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' mlfn' < / span > < span class = "p" > :< / span > < span class = "n" > mlfn< / span > < span class = "p" > ,< / span >
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< span class = "s1" > ' osnet_x1_0' < / span > < span class = "p" > :< / span > < span class = "n" > osnet_x1_0< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' osnet_x0_75' < / span > < span class = "p" > :< / span > < span class = "n" > osnet_x0_75< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' osnet_x0_5' < / span > < span class = "p" > :< / span > < span class = "n" > osnet_x0_5< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' osnet_x0_25' < / span > < span class = "p" > :< / span > < span class = "n" > osnet_x0_25< / span > < span class = "p" > ,< / span >
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< span class = "s1" > ' osnet_ibn_x1_0' < / span > < span class = "p" > :< / span > < span class = "n" > osnet_ibn_x1_0< / span > < span class = "p" > ,< / span >
< span class = "s1" > ' osnet_ain_x1_0' < / span > < span class = "p" > :< / span > < span class = "n" > osnet_ain_x1_0< / span >
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< span class = "p" > }< / span >
< div class = "viewcode-block" id = "show_avai_models" > < a class = "viewcode-back" href = "../../../pkg/models.html#torchreid.models.__init__.show_avai_models" > [docs]< / a > < span class = "k" > def< / span > < span class = "nf" > show_avai_models< / span > < span class = "p" > ():< / span >
< span class = "sd" > " " " Displays available models.< / span >
< span class = "sd" > Examples::< / span >
< span class = "sd" > > > > from torchreid import models< / span >
< span class = "sd" > > > > models.show_avai_models()< / span >
< span class = "sd" > " " " < / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "nb" > list< / span > < span class = "p" > (< / span > < span class = "n" > __model_factory< / span > < span class = "o" > .< / span > < span class = "n" > keys< / span > < span class = "p" > ()))< / span > < / div >
< div class = "viewcode-block" id = "build_model" > < a class = "viewcode-back" href = "../../../pkg/models.html#torchreid.models.__init__.build_model" > [docs]< / a > < span class = "k" > def< / span > < span class = "nf" > build_model< / span > < span class = "p" > (< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > num_classes< / span > < span class = "p" > ,< / span > < span class = "n" > loss< / span > < span class = "o" > =< / span > < span class = "s1" > ' softmax' < / span > < span class = "p" > ,< / span > < span class = "n" > pretrained< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "n" > use_gpu< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " A function wrapper for building a model.< / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > name (str): model name.< / span >
< span class = "sd" > num_classes (int): number of training identities.< / span >
< span class = "sd" > loss (str, optional): loss function to optimize the model. Currently< / span >
< span class = "sd" > supports " softmax" and " triplet" . Default is " softmax" .< / span >
< span class = "sd" > pretrained (bool, optional): whether to load ImageNet-pretrained weights.< / span >
< span class = "sd" > Default is True.< / span >
< span class = "sd" > use_gpu (bool, optional): whether to use gpu. Default is True.< / span >
< span class = "sd" > Returns:< / span >
< span class = "sd" > nn.Module< / span >
< span class = "sd" > Examples::< / span >
< span class = "sd" > > > > from torchreid import models< / span >
< span class = "sd" > > > > model = models.build_model(' resnet50' , 751, loss=' softmax' )< / span >
< span class = "sd" > " " " < / span >
< span class = "n" > avai_models< / span > < span class = "o" > =< / span > < span class = "nb" > list< / span > < span class = "p" > (< / span > < span class = "n" > __model_factory< / span > < span class = "o" > .< / span > < span class = "n" > keys< / span > < span class = "p" > ())< / span >
< span class = "k" > if< / span > < span class = "n" > name< / span > < span class = "ow" > not< / span > < span class = "ow" > in< / span > < span class = "n" > avai_models< / span > < span class = "p" > :< / span >
< span class = "k" > raise< / span > < span class = "ne" > KeyError< / span > < span class = "p" > (< / span > < span class = "s1" > ' Unknown model: < / 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" > name< / span > < span class = "p" > ,< / span > < span class = "n" > avai_models< / span > < span class = "p" > ))< / span >
< span class = "k" > return< / span > < span class = "n" > __model_factory< / span > < span class = "p" > [< / span > < span class = "n" > name< / span > < span class = "p" > ](< / span >
< span class = "n" > num_classes< / span > < span class = "o" > =< / span > < span class = "n" > num_classes< / span > < span class = "p" > ,< / span >
< span class = "n" > loss< / span > < span class = "o" > =< / span > < span class = "n" > loss< / span > < span class = "p" > ,< / span >
< span class = "n" > pretrained< / span > < span class = "o" > =< / span > < span class = "n" > pretrained< / span > < span class = "p" > ,< / span >
< span class = "n" > use_gpu< / span > < span class = "o" > =< / span > < span class = "n" > use_gpu< / span >
< span class = "p" > )< / span > < / div >
< / pre > < / div >
< / div >
< / div >
< footer >
< hr / >
< div role = "contentinfo" >
< p >
© Copyright 2019, Kaiyang Zhou
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