(num_classes=1000, pretrained='imagenet')
return model
r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper.
r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper.
r BNInception model architecture from <https : // arxiv . org / pdf / 1502 . 03167 . pdf > _ paper .
def bninception(num_classes=1000, pretrained='imagenet'):
    r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper.
    """
    model = BNInception(num_classes=num_classes)
    if pretrained is not None:
        settings = pretrained_settings['bninception'][pretrained]
        assert num_classes == settings['num_classes'], \
            "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
        model.load_state_dict(model_zoo.load_url(settings['url']))
        model.input_space = settings['input_space']
        model.input_size = settings['input_size']
        model.input_range = settings['input_range']
        model.mean = settings['mean']
        model.std = settings['std']
    return model
def bninception ( num_classes = 1000 , pretrained = 'imagenet' ) : model = BNInception ( num_classes = num_classes ) if pretrained is not None : settings = pretrained_settings [ 'bninception' ] [ pretrained ] assert num_classes == settings [ 'num_classes' ] , "num_classes should be {}, but is {}" . format ( settings [ 'num_classes' ] , num_classes ) model . load_state_dict ( model_zoo . load_url ( settings [ 'url' ] ) ) model . input_space = settings [ 'input_space' ] model . input_size = settings [ 'input_size' ] model . input_range = settings [ 'input_range' ] model . mean = settings [ 'mean' ] model . std = settings [ 'std' ] return model