examples/torch/common/models/classification/mobilenet_v2_cifar10.py
def mobilenet_v2_cifar10(pretrained=False, progress=True, device="cpu", **kwargs):
    """
    Constructs a MobileNetV2 architecture from
    `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    model = MobileNetV2(**kwargs)
    if pretrained:
        script_dir = os.path.dirname(__file__)
        state_dict = torch.load(
            script_dir + "/state_dicts/mobilenet_v2.pt", map_location=device
        )
        model.load_state_dict(state_dict)
    return model

    Constructs a MobileNetV2 architecture from
    `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    
Constructs a MobileNetV2 architecture from
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" `_.
Constructs a MobileNetV2 architecture from ` " MobileNetV2 : Inverted Residuals and Linear Bottlenecks " ` _ .
def mobilenet_v2_cifar10(pretrained=False, progress=True, device="cpu", **kwargs):
    model = MobileNetV2(**kwargs)
    if pretrained:
        script_dir = os.path.dirname(__file__)
        state_dict = torch.load(
            script_dir + "/state_dicts/mobilenet_v2.pt", map_location=device
        )
        model.load_state_dict(state_dict)
    return model
def mobilenet_v2_cifar10 ( pretrained = False , progress = True , device = "cpu" , ** kwargs ) : model = MobileNetV2 ( ** kwargs ) if pretrained : script_dir = os . path . dirname ( __file__ ) state_dict = torch . load ( script_dir + "/state_dicts/mobilenet_v2.pt" , map_location = device ) model . load_state_dict ( state_dict ) return model
Constructs a MobileNetV2 architecture from
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
Constructs a MobileNetV2 architecture from ` " MobileNetV2 : Inverted Residuals and Linear Bottlenecks " <https : // arxiv . org / abs / 1801 . 04381 > ` _ .
"""
    Constructs a MobileNetV2 architecture from
    `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
pretrained
If True, returns a model pre-trained on ImageNet
If True returns a model pre - trained on ImageNet
progress
If True, displays a progress bar of the download to stderr
If True displays a progress bar of the download to stderr
device