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2019-03-25 01:22:43 +08:00
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< h1 > Source code for torchreid.models.senet< / 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" > division< / span >
< span class = "n" > __all__< / span > < span class = "o" > =< / span > < span class = "p" > [< / span >
< span class = "s1" > ' senet154' < / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnet50' < / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnet101' < / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnet152' < / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnext50_32x4d' < / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnext101_32x4d' < / span > < span class = "p" > ,< / span >
< span class = "s1" > ' se_resnet50_fc512' < / span >
< span class = "p" > ]< / span >
< span class = "kn" > from< / span > < span class = "nn" > collections< / span > < span class = "k" > import< / span > < span class = "n" > OrderedDict< / span >
< span class = "kn" > import< / span > < span class = "nn" > math< / 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 = "kn" > from< / span > < span class = "nn" > torch.utils< / span > < span class = "k" > import< / span > < span class = "n" > model_zoo< / 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 >
< span class = "kn" > import< / span > < span class = "nn" > torchvision< / span >
< span class = "sd" > " " " < / span >
< span class = "sd" > Code imported from https://github.com/Cadene/pretrained-models.pytorch< / span >
< span class = "sd" > " " " < / span >
< span class = "n" > pretrained_settings< / span > < span class = "o" > =< / span > < span class = "p" > {< / span >
< span class = "s1" > ' senet154' < / span > < span class = "p" > :< / span > < span class = "p" > {< / span >
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< span class = "s1" > ' url' < / span > < span class = "p" > :< / span > < span class = "s1" > ' http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth' < / span > < span class = "p" > ,< / span >
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< span class = "s1" > ' se_resnet101' < / span > < span class = "p" > :< / span > < span class = "p" > {< / span >
< span class = "s1" > ' imagenet' < / span > < span class = "p" > :< / span > < span class = "p" > {< / span >
< span class = "s1" > ' url' < / span > < span class = "p" > :< / span > < span class = "s1" > ' http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth' < / span > < span class = "p" > ,< / span >
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< span class = "nb" > super< / span > < span class = "p" > (< / span > < span class = "n" > SEModule< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "fm" > __init__< / span > < span class = "p" > ()< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > avg_pool< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > AdaptiveAvgPool2d< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > fc1< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > channels< / span > < span class = "p" > ,< / span > < span class = "n" > channels< / span > < span class = "o" > //< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > relu< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > fc2< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > channels< / span > < span class = "o" > //< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span > < span class = "n" > channels< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > sigmoid< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Sigmoid< / span > < span class = "p" > ()< / span >
< span class = "k" > def< / span > < span class = "nf" > forward< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "n" > module_input< / span > < span class = "o" > =< / span > < span class = "n" > x< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > avg_pool< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > fc1< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > relu< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > fc2< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > sigmoid< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > module_input< / span > < span class = "o" > *< / span > < span class = "n" > x< / span >
< span class = "k" > class< / span > < span class = "nc" > Bottleneck< / 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 = "sd" > " " " < / span >
< span class = "sd" > Base class for bottlenecks that implements `forward()` method.< / span >
< span class = "sd" > " " " < / span >
< span class = "k" > def< / span > < span class = "nf" > forward< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "n" > residual< / span > < span class = "o" > =< / span > < span class = "n" > x< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv1< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn1< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > relu< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv2< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn2< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > relu< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv3< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn3< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > downsample< / span > < span class = "ow" > is< / span > < span class = "ow" > not< / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
< span class = "n" > residual< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > downsample< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > se_module< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span > < span class = "o" > +< / span > < span class = "n" > residual< / span >
< span class = "n" > out< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > relu< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > out< / span >
< span class = "k" > class< / span > < span class = "nc" > SEBottleneck< / span > < span class = "p" > (< / span > < span class = "n" > Bottleneck< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " < / span >
< span class = "sd" > Bottleneck for SENet154.< / span >
< span class = "sd" > " " " < / span >
< span class = "n" > expansion< / span > < span class = "o" > =< / span > < span class = "mi" > 4< / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ):< / span >
< span class = "nb" > super< / span > < span class = "p" > (< / span > < span class = "n" > SEBottleneck< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "fm" > __init__< / span > < span class = "p" > ()< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv1< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn1< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 2< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv2< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span >
< span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "n" > stride< / span > < span class = "p" > ,< / span > < span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span >
< span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn2< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv3< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn3< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > relu< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > se_module< / span > < span class = "o" > =< / span > < span class = "n" > SEModule< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "n" > reduction< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > downsample< / span > < span class = "o" > =< / span > < span class = "n" > downsample< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "n" > stride< / span >
< span class = "k" > class< / span > < span class = "nc" > SEResNetBottleneck< / span > < span class = "p" > (< / span > < span class = "n" > Bottleneck< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " < / span >
< span class = "sd" > ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe< / span >
< span class = "sd" > implementation and uses `stride=stride` in `conv1` and not in `conv2`< / span >
< span class = "sd" > (the latter is used in the torchvision implementation of ResNet).< / span >
< span class = "sd" > " " " < / span >
< span class = "n" > expansion< / span > < span class = "o" > =< / span > < span class = "mi" > 4< / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ):< / span >
< span class = "nb" > super< / span > < span class = "p" > (< / span > < span class = "n" > SEResNetBottleneck< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "fm" > __init__< / span > < span class = "p" > ()< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv1< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "n" > stride< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn1< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv2< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn2< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv3< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn3< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > relu< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > se_module< / span > < span class = "o" > =< / span > < span class = "n" > SEModule< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "n" > reduction< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > downsample< / span > < span class = "o" > =< / span > < span class = "n" > downsample< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "n" > stride< / span >
< span class = "k" > class< / span > < span class = "nc" > SEResNeXtBottleneck< / span > < span class = "p" > (< / span > < span class = "n" > Bottleneck< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " ResNeXt bottleneck type C with a Squeeze-and-Excitation module" " " < / span >
< span class = "n" > expansion< / span > < span class = "o" > =< / span > < span class = "mi" > 4< / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span > < span class = "n" > base_width< / span > < span class = "o" > =< / span > < span class = "mi" > 4< / span > < span class = "p" > ):< / span >
< span class = "nb" > super< / span > < span class = "p" > (< / span > < span class = "n" > SEResNeXtBottleneck< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "fm" > __init__< / span > < span class = "p" > ()< / span >
< span class = "n" > width< / span > < span class = "o" > =< / span > < span class = "nb" > int< / span > < span class = "p" > (< / span > < span class = "n" > math< / span > < span class = "o" > .< / span > < span class = "n" > floor< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "p" > (< / span > < span class = "n" > base_width< / span > < span class = "o" > /< / span > < span class = "mf" > 64.< / span > < span class = "p" > ))< / span > < span class = "o" > *< / span > < span class = "n" > groups< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv1< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn1< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > width< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv2< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > width< / span > < span class = "p" > ,< / span > < span class = "n" > width< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "n" > stride< / span > < span class = "p" > ,< / span >
< span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn2< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > width< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > conv3< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "n" > width< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > bn3< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > relu< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > se_module< / span > < span class = "o" > =< / span > < span class = "n" > SEModule< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "n" > reduction< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > downsample< / span > < span class = "o" > =< / span > < span class = "n" > downsample< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "n" > stride< / span >
< div class = "viewcode-block" id = "SENet" > < a class = "viewcode-back" href = "../../../pkg/models.html#torchreid.models.senet.SENet" > [docs]< / a > < span class = "k" > class< / span > < span class = "nc" > SENet< / 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 = "sd" > " " " Squeeze-and-excitation network.< / span >
< span class = "sd" > < / span >
< span class = "sd" > Reference:< / span >
< span class = "sd" > Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.< / span >
< span class = "sd" > Public keys:< / span >
< span class = "sd" > - ``senet154``: SENet154.< / span >
< span class = "sd" > - ``se_resnet50``: ResNet50 + SE.< / span >
< span class = "sd" > - ``se_resnet101``: ResNet101 + SE.< / span >
< span class = "sd" > - ``se_resnet152``: ResNet152 + SE.< / span >
< span class = "sd" > - ``se_resnext50_32x4d``: ResNeXt50 (groups=32, width=4) + SE.< / span >
< span class = "sd" > - ``se_resnext101_32x4d``: ResNeXt101 (groups=32, width=4) + SE.< / span >
< span class = "sd" > - ``se_resnet50_fc512``: (ResNet50 + SE) + FC.< / span >
< span class = "sd" > " " " < / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > num_classes< / span > < span class = "p" > ,< / span > < span class = "n" > loss< / span > < span class = "p" > ,< / span > < span class = "n" > block< / span > < span class = "p" > ,< / span > < span class = "n" > layers< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span > < span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "mf" > 0.2< / span > < span class = "p" > ,< / span >
< span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "mi" > 128< / span > < span class = "p" > ,< / span > < span class = "n" > input_3x3< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > last_stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "n" > fc_dims< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span > < span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " < / span >
< span class = "sd" > Parameters< / span >
< span class = "sd" > ----------< / span >
< span class = "sd" > block (nn.Module): Bottleneck class.< / span >
< span class = "sd" > - For SENet154: SEBottleneck< / span >
< span class = "sd" > - For SE-ResNet models: SEResNetBottleneck< / span >
< span class = "sd" > - For SE-ResNeXt models: SEResNeXtBottleneck< / span >
< span class = "sd" > layers (list of ints): Number of residual blocks for 4 layers of the< / span >
< span class = "sd" > network (layer1...layer4).< / span >
< span class = "sd" > groups (int): Number of groups for the 3x3 convolution in each< / span >
< span class = "sd" > bottleneck block.< / span >
< span class = "sd" > - For SENet154: 64< / span >
< span class = "sd" > - For SE-ResNet models: 1< / span >
< span class = "sd" > - For SE-ResNeXt models: 32< / span >
< span class = "sd" > reduction (int): Reduction ratio for Squeeze-and-Excitation modules.< / span >
< span class = "sd" > - For all models: 16< / span >
< span class = "sd" > dropout_p (float or None): Drop probability for the Dropout layer.< / span >
< span class = "sd" > If `None` the Dropout layer is not used.< / span >
< span class = "sd" > - For SENet154: 0.2< / span >
< span class = "sd" > - For SE-ResNet models: None< / span >
< span class = "sd" > - For SE-ResNeXt models: None< / span >
< span class = "sd" > inplanes (int): Number of input channels for layer1.< / span >
< span class = "sd" > - For SENet154: 128< / span >
< span class = "sd" > - For SE-ResNet models: 64< / span >
< span class = "sd" > - For SE-ResNeXt models: 64< / span >
< span class = "sd" > input_3x3 (bool): If `True`, use three 3x3 convolutions instead of< / span >
< span class = "sd" > a single 7x7 convolution in layer0.< / span >
< span class = "sd" > - For SENet154: True< / span >
< span class = "sd" > - For SE-ResNet models: False< / span >
< span class = "sd" > - For SE-ResNeXt models: False< / span >
< span class = "sd" > downsample_kernel_size (int): Kernel size for downsampling convolutions< / span >
< span class = "sd" > in layer2, layer3 and layer4.< / span >
< span class = "sd" > - For SENet154: 3< / span >
< span class = "sd" > - For SE-ResNet models: 1< / span >
< span class = "sd" > - For SE-ResNeXt models: 1< / span >
< span class = "sd" > downsample_padding (int): Padding for downsampling convolutions in< / span >
< span class = "sd" > layer2, layer3 and layer4.< / span >
< span class = "sd" > - For SENet154: 1< / span >
< span class = "sd" > - For SE-ResNet models: 0< / span >
< span class = "sd" > - For SE-ResNeXt models: 0< / span >
< span class = "sd" > num_classes (int): Number of outputs in `classifier` layer.< / span >
< span class = "sd" > " " " < / span >
< span class = "nb" > super< / span > < span class = "p" > (< / span > < span class = "n" > SENet< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "fm" > __init__< / span > < span class = "p" > ()< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "n" > inplanes< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > loss< / span > < span class = "o" > =< / span > < span class = "n" > loss< / span >
< span class = "k" > if< / span > < span class = "n" > input_3x3< / span > < span class = "p" > :< / span >
< span class = "n" > layer0_modules< / span > < span class = "o" > =< / span > < span class = "p" > [< / span >
< span class = "p" > (< / span > < span class = "s1" > ' conv1' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' bn1' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "mi" > 64< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' relu1' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' conv2' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' bn2' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "mi" > 64< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' relu2' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' conv3' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' bn3' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > inplanes< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' relu3' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )),< / span >
< span class = "p" > ]< / span >
< span class = "k" > else< / span > < span class = "p" > :< / span >
< span class = "n" > layer0_modules< / span > < span class = "o" > =< / span > < span class = "p" > [< / span >
< span class = "p" > (< / span > < span class = "s1" > ' conv1' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 7< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' bn1' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > inplanes< / span > < span class = "p" > )),< / span >
< span class = "p" > (< / span > < span class = "s1" > ' relu1' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )),< / span >
< span class = "p" > ]< / span >
< span class = "c1" > # To preserve compatibility with Caffe weights `ceil_mode=True`< / span >
< span class = "c1" > # is used instead of `padding=1`.< / span >
< span class = "n" > layer0_modules< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > ((< / span > < span class = "s1" > ' pool' < / span > < span class = "p" > ,< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > MaxPool2d< / span > < span class = "p" > (< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > ceil_mode< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )))< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer0< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Sequential< / span > < span class = "p" > (< / span > < span class = "n" > OrderedDict< / span > < span class = "p" > (< / span > < span class = "n" > layer0_modules< / span > < span class = "p" > ))< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer1< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > _make_layer< / span > < span class = "p" > (< / span >
< span class = "n" > block< / span > < span class = "p" > ,< / span >
< span class = "n" > planes< / span > < span class = "o" > =< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span >
< span class = "n" > blocks< / span > < span class = "o" > =< / span > < span class = "n" > layers< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ],< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span >
< span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer2< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > _make_layer< / span > < span class = "p" > (< / span >
< span class = "n" > block< / span > < span class = "p" > ,< / span >
< span class = "n" > planes< / span > < span class = "o" > =< / span > < span class = "mi" > 128< / span > < span class = "p" > ,< / span >
< span class = "n" > blocks< / span > < span class = "o" > =< / span > < span class = "n" > layers< / span > < span class = "p" > [< / span > < span class = "mi" > 1< / span > < span class = "p" > ],< / span >
< span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "n" > downsample_kernel_size< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "n" > downsample_padding< / span >
< span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer3< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > _make_layer< / span > < span class = "p" > (< / span >
< span class = "n" > block< / span > < span class = "p" > ,< / span >
< span class = "n" > planes< / span > < span class = "o" > =< / span > < span class = "mi" > 256< / span > < span class = "p" > ,< / span >
< span class = "n" > blocks< / span > < span class = "o" > =< / span > < span class = "n" > layers< / span > < span class = "p" > [< / span > < span class = "mi" > 2< / span > < span class = "p" > ],< / span >
< span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "n" > downsample_kernel_size< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "n" > downsample_padding< / span >
< span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer4< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > _make_layer< / span > < span class = "p" > (< / span >
< span class = "n" > block< / span > < span class = "p" > ,< / span >
< span class = "n" > planes< / span > < span class = "o" > =< / span > < span class = "mi" > 512< / span > < span class = "p" > ,< / span >
< span class = "n" > blocks< / span > < span class = "o" > =< / span > < span class = "n" > layers< / span > < span class = "p" > [< / span > < span class = "mi" > 3< / span > < span class = "p" > ],< / span >
< span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "n" > last_stride< / span > < span class = "p" > ,< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "n" > downsample_kernel_size< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "n" > downsample_padding< / span >
< span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > global_avgpool< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > AdaptiveAvgPool2d< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > fc< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > _construct_fc_layer< / span > < span class = "p" > (< / span > < span class = "n" > fc_dims< / span > < span class = "p" > ,< / span > < span class = "mi" > 512< / span > < span class = "o" > *< / span > < span class = "n" > block< / span > < span class = "o" > .< / span > < span class = "n" > expansion< / span > < span class = "p" > ,< / span > < span class = "n" > dropout_p< / span > < span class = "p" > )< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > classifier< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Linear< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > feature_dim< / span > < span class = "p" > ,< / span > < span class = "n" > num_classes< / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > _make_layer< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > block< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > blocks< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ):< / span >
< span class = "n" > downsample< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span >
< span class = "k" > if< / span > < span class = "n" > stride< / span > < span class = "o" > !=< / span > < span class = "mi" > 1< / span > < span class = "ow" > or< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > inplanes< / span > < span class = "o" > !=< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "n" > block< / span > < span class = "o" > .< / span > < span class = "n" > expansion< / span > < span class = "p" > :< / span >
< span class = "n" > downsample< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Sequential< / span > < span class = "p" > (< / span >
< span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Conv2d< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "n" > block< / span > < span class = "o" > .< / span > < span class = "n" > expansion< / span > < span class = "p" > ,< / span >
< span class = "n" > kernel_size< / span > < span class = "o" > =< / span > < span class = "n" > downsample_kernel_size< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "o" > =< / span > < span class = "n" > stride< / span > < span class = "p" > ,< / span >
< span class = "n" > padding< / span > < span class = "o" > =< / span > < span class = "n" > downsample_padding< / span > < span class = "p" > ,< / span > < span class = "n" > bias< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ),< / span >
< span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm2d< / span > < span class = "p" > (< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "n" > block< / span > < span class = "o" > .< / span > < span class = "n" > expansion< / span > < span class = "p" > ),< / span >
< span class = "p" > )< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
< span class = "n" > layers< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > (< / span > < span class = "n" > block< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "p" > ,< / span > < span class = "n" > stride< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample< / span > < span class = "p" > ))< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "n" > planes< / span > < span class = "o" > *< / span > < span class = "n" > block< / span > < span class = "o" > .< / span > < span class = "n" > expansion< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > blocks< / span > < span class = "p" > ):< / span >
< span class = "n" > layers< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > (< / span > < span class = "n" > block< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > inplanes< / span > < span class = "p" > ,< / span > < span class = "n" > planes< / span > < span class = "p" > ,< / span > < span class = "n" > groups< / span > < span class = "p" > ,< / span > < span class = "n" > reduction< / span > < span class = "p" > ))< / span >
< span class = "k" > return< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Sequential< / span > < span class = "p" > (< / span > < span class = "o" > *< / span > < span class = "n" > layers< / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > _construct_fc_layer< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > fc_dims< / span > < span class = "p" > ,< / span > < span class = "n" > input_dim< / span > < span class = "p" > ,< / span > < span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " < / span >
< span class = "sd" > Construct fully connected layer< / span >
< span class = "sd" > - fc_dims (list or tuple): dimensions of fc layers, if None,< / span >
< span class = "sd" > no fc layers are constructed< / span >
< span class = "sd" > - input_dim (int): input dimension< / span >
< span class = "sd" > - dropout_p (float): dropout probability, if None, dropout is unused< / span >
< span class = "sd" > " " " < / span >
< span class = "k" > if< / span > < span class = "n" > fc_dims< / span > < span class = "ow" > is< / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > feature_dim< / span > < span class = "o" > =< / span > < span class = "n" > input_dim< / span >
< span class = "k" > return< / span > < span class = "kc" > None< / span >
< span class = "k" > assert< / span > < span class = "nb" > isinstance< / span > < span class = "p" > (< / span > < span class = "n" > fc_dims< / span > < span class = "p" > ,< / span > < span class = "p" > (< / span > < span class = "nb" > list< / span > < span class = "p" > ,< / span > < span class = "nb" > tuple< / span > < span class = "p" > )),< / span > < span class = "s1" > ' fc_dims must be either list or tuple, but got < / span > < span class = "si" > {}< / span > < span class = "s1" > ' < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "nb" > type< / span > < span class = "p" > (< / span > < span class = "n" > fc_dims< / span > < span class = "p" > ))< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
< span class = "k" > for< / span > < span class = "n" > dim< / span > < span class = "ow" > in< / span > < span class = "n" > fc_dims< / span > < span class = "p" > :< / span >
< span class = "n" > layers< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > (< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Linear< / span > < span class = "p" > (< / span > < span class = "n" > input_dim< / span > < span class = "p" > ,< / span > < span class = "n" > dim< / span > < span class = "p" > ))< / span >
< span class = "n" > layers< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > (< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > BatchNorm1d< / span > < span class = "p" > (< / span > < span class = "n" > dim< / span > < span class = "p" > ))< / span >
< span class = "n" > layers< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > (< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > ReLU< / span > < span class = "p" > (< / span > < span class = "n" > inplace< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ))< / span >
< span class = "k" > if< / span > < span class = "n" > dropout_p< / span > < span class = "ow" > is< / span > < span class = "ow" > not< / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
< span class = "n" > layers< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > (< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Dropout< / span > < span class = "p" > (< / span > < span class = "n" > p< / span > < span class = "o" > =< / span > < span class = "n" > dropout_p< / span > < span class = "p" > ))< / span >
< span class = "n" > input_dim< / span > < span class = "o" > =< / span > < span class = "n" > dim< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > feature_dim< / span > < span class = "o" > =< / span > < span class = "n" > fc_dims< / span > < span class = "p" > [< / span > < span class = "o" > -< / span > < span class = "mi" > 1< / span > < span class = "p" > ]< / span >
< span class = "k" > return< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Sequential< / span > < span class = "p" > (< / span > < span class = "o" > *< / span > < span class = "n" > layers< / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > featuremaps< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer0< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer1< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer2< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer3< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layer4< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > x< / span >
< span class = "k" > def< / span > < span class = "nf" > forward< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "n" > f< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > featuremaps< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > v< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > global_avgpool< / span > < span class = "p" > (< / span > < span class = "n" > f< / span > < span class = "p" > )< / span >
< span class = "n" > v< / span > < span class = "o" > =< / span > < span class = "n" > v< / span > < span class = "o" > .< / span > < span class = "n" > view< / span > < span class = "p" > (< / span > < span class = "n" > v< / 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 = "o" > -< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > fc< / span > < span class = "ow" > is< / span > < span class = "ow" > not< / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
< span class = "n" > v< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > fc< / span > < span class = "p" > (< / span > < span class = "n" > v< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "ow" > not< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > training< / span > < span class = "p" > :< / span >
< span class = "k" > return< / span > < span class = "n" > v< / span >
< span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > classifier< / span > < span class = "p" > (< / span > < span class = "n" > v< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > loss< / span > < span class = "o" > ==< / span > < span class = "s1" > ' softmax' < / span > < span class = "p" > :< / span >
< span class = "k" > return< / span > < span class = "n" > y< / span >
< span class = "k" > elif< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > loss< / span > < span class = "o" > ==< / span > < span class = "s1" > ' triplet' < / span > < span class = "p" > :< / span >
< span class = "k" > return< / span > < span class = "n" > y< / span > < span class = "p" > ,< / span > < span class = "n" > v< / span >
< span class = "k" > else< / span > < span class = "p" > :< / span >
< span class = "k" > raise< / span > < span class = "ne" > KeyError< / span > < span class = "p" > (< / span > < span class = "s2" > " Unsupported loss: < / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > loss< / span > < span class = "p" > ))< / span > < / div >
< span class = "k" > def< / span > < span class = "nf" > init_pretrained_weights< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > model_url< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " Initializes model with pretrained weights.< / span >
< span class = "sd" > < / span >
< span class = "sd" > Layers that don' t match with pretrained layers in name or size are kept unchanged.< / span >
< span class = "sd" > " " " < / span >
< span class = "n" > pretrain_dict< / span > < span class = "o" > =< / span > < span class = "n" > model_zoo< / span > < span class = "o" > .< / span > < span class = "n" > load_url< / span > < span class = "p" > (< / span > < span class = "n" > model_url< / span > < span class = "p" > )< / span >
< span class = "n" > model_dict< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > state_dict< / span > < span class = "p" > ()< / span >
< span class = "n" > pretrain_dict< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "n" > k< / span > < span class = "p" > :< / span > < span class = "n" > v< / span > < span class = "k" > for< / span > < span class = "n" > k< / span > < span class = "p" > ,< / span > < span class = "n" > v< / span > < span class = "ow" > in< / span > < span class = "n" > pretrain_dict< / span > < span class = "o" > .< / span > < span class = "n" > items< / span > < span class = "p" > ()< / span > < span class = "k" > if< / span > < span class = "n" > k< / span > < span class = "ow" > in< / span > < span class = "n" > model_dict< / span > < span class = "ow" > and< / span > < span class = "n" > model_dict< / span > < span class = "p" > [< / span > < span class = "n" > k< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()< / span > < span class = "o" > ==< / span > < span class = "n" > v< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ()}< / span >
< span class = "n" > model_dict< / span > < span class = "o" > .< / span > < span class = "n" > update< / span > < span class = "p" > (< / span > < span class = "n" > pretrain_dict< / span > < span class = "p" > )< / span >
< span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > load_state_dict< / span > < span class = "p" > (< / span > < span class = "n" > model_dict< / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > senet154< / 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 = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > SENet< / 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" > block< / span > < span class = "o" > =< / span > < span class = "n" > SEBottleneck< / span > < span class = "p" > ,< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 8< / span > < span class = "p" > ,< / span > < span class = "mi" > 36< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ],< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "mi" > 16< / span > < span class = "p" > ,< / span >
< span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "mf" > 0.2< / span > < span class = "p" > ,< / span >
< span class = "n" > last_stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > fc_dims< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "o" > **< / span > < span class = "n" > kwargs< / span >
< span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > pretrained< / span > < span class = "p" > :< / span >
< span class = "n" > model_url< / span > < span class = "o" > =< / span > < span class = "n" > pretrained_settings< / span > < span class = "p" > [< / span > < span class = "s1" > ' senet154' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' imagenet' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' url' < / span > < span class = "p" > ]< / span >
< span class = "n" > init_pretrained_weights< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > model_url< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > model< / span >
< span class = "k" > def< / span > < span class = "nf" > se_resnet50< / 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 = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > SENet< / 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" > block< / span > < span class = "o" > =< / span > < span class = "n" > SEResNetBottleneck< / span > < span class = "p" > ,< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "mi" > 6< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ],< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "mi" > 16< / span > < span class = "p" > ,< / span >
< span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span >
< span class = "n" > input_3x3< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span >
< span class = "n" > last_stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > fc_dims< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "o" > **< / span > < span class = "n" > kwargs< / span >
< span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > pretrained< / span > < span class = "p" > :< / span >
< span class = "n" > model_url< / span > < span class = "o" > =< / span > < span class = "n" > pretrained_settings< / span > < span class = "p" > [< / span > < span class = "s1" > ' se_resnet50' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' imagenet' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' url' < / span > < span class = "p" > ]< / span >
< span class = "n" > init_pretrained_weights< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > model_url< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > model< / span >
< span class = "k" > def< / span > < span class = "nf" > se_resnet50_fc512< / 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 = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > SENet< / 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" > block< / span > < span class = "o" > =< / span > < span class = "n" > SEResNetBottleneck< / span > < span class = "p" > ,< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "mi" > 6< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ],< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "mi" > 16< / span > < span class = "p" > ,< / span >
< span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span >
< span class = "n" > input_3x3< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span >
< span class = "n" > last_stride< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > fc_dims< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 512< / span > < span class = "p" > ],< / span >
< span class = "o" > **< / span > < span class = "n" > kwargs< / span >
< span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > pretrained< / span > < span class = "p" > :< / span >
< span class = "n" > model_url< / span > < span class = "o" > =< / span > < span class = "n" > pretrained_settings< / span > < span class = "p" > [< / span > < span class = "s1" > ' se_resnet50' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' imagenet' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' url' < / span > < span class = "p" > ]< / span >
< span class = "n" > init_pretrained_weights< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > model_url< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > model< / span >
< span class = "k" > def< / span > < span class = "nf" > se_resnet101< / 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 = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > SENet< / 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" > block< / span > < span class = "o" > =< / span > < span class = "n" > SEResNetBottleneck< / span > < span class = "p" > ,< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "mi" > 23< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ],< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "mi" > 16< / span > < span class = "p" > ,< / span >
< span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span >
< span class = "n" > input_3x3< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span >
< span class = "n" > last_stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > fc_dims< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "o" > **< / span > < span class = "n" > kwargs< / span >
< span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > pretrained< / span > < span class = "p" > :< / span >
< span class = "n" > model_url< / span > < span class = "o" > =< / span > < span class = "n" > pretrained_settings< / span > < span class = "p" > [< / span > < span class = "s1" > ' se_resnet101' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' imagenet' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' url' < / span > < span class = "p" > ]< / span >
< span class = "n" > init_pretrained_weights< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > model_url< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > model< / span >
< span class = "k" > def< / span > < span class = "nf" > se_resnet152< / 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 = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > SENet< / 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" > block< / span > < span class = "o" > =< / span > < span class = "n" > SEResNetBottleneck< / span > < span class = "p" > ,< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 8< / span > < span class = "p" > ,< / span > < span class = "mi" > 36< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ],< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "mi" > 16< / span > < span class = "p" > ,< / span >
< span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span >
< span class = "n" > input_3x3< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span >
< span class = "n" > last_stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > fc_dims< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "o" > **< / span > < span class = "n" > kwargs< / span >
< span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > pretrained< / span > < span class = "p" > :< / span >
< span class = "n" > model_url< / span > < span class = "o" > =< / span > < span class = "n" > pretrained_settings< / span > < span class = "p" > [< / span > < span class = "s1" > ' se_resnet152' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' imagenet' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' url' < / span > < span class = "p" > ]< / span >
< span class = "n" > init_pretrained_weights< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > model_url< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > model< / span >
< span class = "k" > def< / span > < span class = "nf" > se_resnext50_32x4d< / 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 = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > SENet< / 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" > block< / span > < span class = "o" > =< / span > < span class = "n" > SEResNeXtBottleneck< / span > < span class = "p" > ,< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "mi" > 6< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ],< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "mi" > 32< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "mi" > 16< / span > < span class = "p" > ,< / span >
< span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span >
< span class = "n" > input_3x3< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span >
< span class = "n" > last_stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > fc_dims< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "o" > **< / span > < span class = "n" > kwargs< / span >
< span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > pretrained< / span > < span class = "p" > :< / span >
< span class = "n" > model_url< / span > < span class = "o" > =< / span > < span class = "n" > pretrained_settings< / span > < span class = "p" > [< / span > < span class = "s1" > ' se_resnext50_32x4d' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' imagenet' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' url' < / span > < span class = "p" > ]< / span >
< span class = "n" > init_pretrained_weights< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > model_url< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > model< / span >
< span class = "k" > def< / span > < span class = "nf" > se_resnext101_32x4d< / 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 = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > SENet< / 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" > block< / span > < span class = "o" > =< / span > < span class = "n" > SEResNeXtBottleneck< / span > < span class = "p" > ,< / span >
< span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "mi" > 23< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ],< / span >
< span class = "n" > groups< / span > < span class = "o" > =< / span > < span class = "mi" > 32< / span > < span class = "p" > ,< / span >
< span class = "n" > reduction< / span > < span class = "o" > =< / span > < span class = "mi" > 16< / span > < span class = "p" > ,< / span >
< span class = "n" > dropout_p< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "n" > inplanes< / span > < span class = "o" > =< / span > < span class = "mi" > 64< / span > < span class = "p" > ,< / span >
< span class = "n" > input_3x3< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_kernel_size< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span >
< span class = "n" > downsample_padding< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span >
< span class = "n" > last_stride< / span > < span class = "o" > =< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span >
< span class = "n" > fc_dims< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span >
< span class = "o" > **< / span > < span class = "n" > kwargs< / span >
< span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > pretrained< / span > < span class = "p" > :< / span >
< span class = "n" > model_url< / span > < span class = "o" > =< / span > < span class = "n" > pretrained_settings< / span > < span class = "p" > [< / span > < span class = "s1" > ' se_resnext101_32x4d' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' imagenet' < / span > < span class = "p" > ][< / span > < span class = "s1" > ' url' < / span > < span class = "p" > ]< / span >
< span class = "n" > init_pretrained_weights< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > model_url< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > model< / span >
< / pre > < / div >
< / div >
< / div >
< footer >
< hr / >
< div role = "contentinfo" >
< p >
© Copyright 2019, Kaiyang Zhou
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SphinxRtdTheme.Navigation.enable(true);
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