torchvision/models/inception.py
def inception_v3(pretrained=False, **kwargs):
    r"""Inception v3 model architecture from
    `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.

    .. note::
        **Important**: In contrast to the other models the inception_v3 expects tensors with a size of
        N x 3 x 299 x 299, so ensure your images are sized accordingly.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        aux_logits (bool): If True, add an auxiliary branch that can improve training.
            Default: *True*
        transform_input (bool): If True, preprocesses the input according to the method with which it
            was trained on ImageNet. Default: *False*
    """
    if pretrained:
        if 'transform_input' not in kwargs:
            kwargs['transform_input'] = True
        if 'aux_logits' in kwargs:
            original_aux_logits = kwargs['aux_logits']
            kwargs['aux_logits'] = True
        else:
            original_aux_logits = True
        model = Inception3(**kwargs)
        model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google']))
        if not original_aux_logits:
            model.aux_logits = False
            del model.AuxLogits
        return model

    return Inception3(**kwargs)
def inception_v3(pretrained=False, **kwargs):
    r"""Inception v3 model architecture from
    `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.

    .. note::
        **Important**: In contrast to the other models the inception_v3 expects tensors with a size of
        N x 3 x 299 x 299, so ensure your images are sized accordingly.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        aux_logits (bool): If True, add an auxiliary branch that can improve training.
            Default: *True*
        transform_input (bool): If True, preprocesses the input according to the method with which it
            was trained on ImageNet. Default: *False*
    """
    if pretrained:
        if 'transform_input' not in kwargs:
            kwargs['transform_input'] = True
        if 'aux_logits' in kwargs:
            original_aux_logits = kwargs['aux_logits']
            kwargs['aux_logits'] = True
        else:
            original_aux_logits = True
        model = Inception3(**kwargs)
        model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google']))
        if not original_aux_logits:
            model.aux_logits = False
            del model.AuxLogits
        return model

    return Inception3(**kwargs)
def inception_v3 ( pretrained = False , * * kwargs ) : if pretrained : if 'transform_input' not in kwargs : kwargs [ 'transform_input' ] = True if 'aux_logits' in kwargs : original_aux_logits = kwargs [ 'aux_logits' ] kwargs [ 'aux_logits' ] = True else : original_aux_logits = True model = Inception3 ( * * kwargs ) model . load_state_dict ( model_zoo . load_url ( model_urls [ 'inception_v3_google' ] ) ) if not original_aux_logits : model . aux_logits = False del model . AuxLogits return model return Inception3 ( * * kwargs )
r"""Inception v3 model architecture from
    `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.

    .. note::
        **Important**: In contrast to the other models the inception_v3 expects tensors with a size of
        N x 3 x 299 x 299, so ensure your images are sized accordingly.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        aux_logits (bool): If True, add an auxiliary branch that can improve training.
            Default: *True*
        transform_input (bool): If True, preprocesses the input according to the method with which it
            was trained on ImageNet. Default: *False*
r Inception v3 model architecture from Rethinking the Inception Architecture for Computer Vision <http : // arxiv . org / abs / 1512 . 00567 > _ .