# torchreid.models.__init__ — torchreid 1.4.0 documentation

# Source code for torchreid.models.__init__

    from __future__ import absolute_import
    import torch

    from .pcb import *
    from .mlfn import *
    from .hacnn import *
    from .osnet import *
    from .senet import *
    from .mudeep import *
    from .nasnet import *
    from .resnet import *
    from .densenet import *
    from .xception import *
    from .osnet_ain import *
    from .resnetmid import *
    from .shufflenet import *
    from .squeezenet import *
    from .inceptionv4 import *
    from .mobilenetv2 import *
    from .resnet_ibn_a import *
    from .resnet_ibn_b import *
    from .shufflenetv2 import *
    from .inceptionresnetv2 import *

    __model_factory = {
        # image classification models
        'resnet18': resnet18,
        'resnet34': resnet34,
        'resnet50': resnet50,
        'resnet101': resnet101,
        'resnet152': resnet152,
        'resnext50_32x4d': resnext50_32x4d,
        'resnext101_32x8d': resnext101_32x8d,
        'resnet50_fc512': resnet50_fc512,
        'se_resnet50': se_resnet50,
        'se_resnet50_fc512': se_resnet50_fc512,
        'se_resnet101': se_resnet101,
        'se_resnext50_32x4d': se_resnext50_32x4d,
        'se_resnext101_32x4d': se_resnext101_32x4d,
        'densenet121': densenet121,
        'densenet169': densenet169,
        'densenet201': densenet201,
        'densenet161': densenet161,
        'densenet121_fc512': densenet121_fc512,
        'inceptionresnetv2': inceptionresnetv2,
        'inceptionv4': inceptionv4,
        'xception': xception,
        'resnet50_ibn_a': resnet50_ibn_a,
        'resnet50_ibn_b': resnet50_ibn_b,
        # lightweight models
        'nasnsetmobile': nasnetamobile,
        'mobilenetv2_x1_0': mobilenetv2_x1_0,
        'mobilenetv2_x1_4': mobilenetv2_x1_4,
        'shufflenet': shufflenet,
        'squeezenet1_0': squeezenet1_0,
        'squeezenet1_0_fc512': squeezenet1_0_fc512,
        'squeezenet1_1': squeezenet1_1,
        'shufflenet_v2_x0_5': shufflenet_v2_x0_5,
        'shufflenet_v2_x1_0': shufflenet_v2_x1_0,
        'shufflenet_v2_x1_5': shufflenet_v2_x1_5,
        'shufflenet_v2_x2_0': shufflenet_v2_x2_0,
        # reid-specific models
        'mudeep': MuDeep,
        'resnet50mid': resnet50mid,
        'hacnn': HACNN,
        'pcb_p6': pcb_p6,
        'pcb_p4': pcb_p4,
        'mlfn': mlfn,
        'osnet_x1_0': osnet_x1_0,
        'osnet_x0_75': osnet_x0_75,
        'osnet_x0_5': osnet_x0_5,
        'osnet_x0_25': osnet_x0_25,
        'osnet_ibn_x1_0': osnet_ibn_x1_0,
        'osnet_ain_x1_0': osnet_ain_x1_0,
        'osnet_ain_x0_75': osnet_ain_x0_75,
        'osnet_ain_x0_5': osnet_ain_x0_5,
        'osnet_ain_x0_25': osnet_ain_x0_25
    }

    def show_avai_models():
        """Displays available models.

        Examples::
            >>> from torchreid import models
            >>> models.show_avai_models()
        """
        print(list(__model_factory.keys()))

    def build_model(
        name, num_classes, loss='softmax', pretrained=True, use_gpu=True
    ):
        """A function wrapper for building a model.

        Args:
            name (str): model name.
            num_classes (int): number of training identities.
            loss (str, optional): loss function to optimize the model. Currently
                supports "softmax" and "triplet". Default is "softmax".
            pretrained (bool, optional): whether to load ImageNet-pretrained weights.
                Default is True.
            use_gpu (bool, optional): whether to use gpu. Default is True.

        Returns:
            nn.Module

        Examples::
            >>> from torchreid import models
            >>> model = models.build_model('resnet50', 751, loss='softmax')
        """
        avai_models = list(__model_factory.keys())
        if name not in avai_models:
            raise KeyError(
                'Unknown model: {}. Must be one of {}'.format(name, avai_models)
            )
        return __model_factory[name](
            num_classes=num_classes,
            loss=loss,
            pretrained=pretrained,
            use_gpu=use_gpu
        )