# Source code for torchreid.models.__init__

    from __future__ import absolute_import

    import torch

    from .resnet import *
    from .resnetmid import *
    from .resnext import *
    from .senet import *
    from .densenet import *
    from .inceptionresnetv2 import *
    from .inceptionv4 import *
    from .xception import *

    from .nasnet import *
    from .mobilenetv2 import *
    from .shufflenet import *
    from .squeezenet import *

    from .mudeep import *
    from .hacnn import *
    from .pcb import *
    from .mlfn import *

    __model_factory = {
        # image classification models
        'resnet18': resnet18,
        'resnet34': resnet34,
        'resnet50': resnet50,
        'resnet101': resnet101,
        'resnet152': resnet152,
        'resnet50_fc512': resnet50_fc512,
        'resnext50_32x4d': resnext50_32x4d,
        'resnext50_32x4d_fc512': resnext50_32x4d_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,
        # lightweight models
        'nasnsetmobile': nasnetamobile,
        'mobilenetv2_1dot0': mobilenetv2_1dot0,
        'mobilenetv2_1dot4': mobilenetv2_1dot4,
        'shufflenet': shufflenet,
        'squeezenet1_0': squeezenet1_0,
        'squeezenet1_0_fc512': squeezenet1_0_fc512,
        'squeezenet1_1': squeezenet1_1,
        # reid-specific models
        'mudeep': MuDeep,
        'resnet50mid': resnet50mid,
        'hacnn': HACNN,
        'pcb_p6': pcb_p6,
        'pcb_p4': pcb_p4,
        'mlfn': mlfn,
    }

    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))
        print('Initializing model: {}'.format(name))
        return __model_factory[name](
            num_classes=num_classes,
            loss=loss,
            pretrained=pretrained,
            use_gpu=use_gpu
        )