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vgg

model.backbone.torchvision.vgg

__all__ module-attribute 

__all__ = ['VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn']

cfgs module-attribute 

cfgs: Dict[str, List[Union[str, int]]] = {'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M']}

VGG 

VGG(features: Module, num_classes: int = 1000, init_weights: bool = True, dropout: float = 0.5)

Bases: Module

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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def __init__(
    self,
    features: nn.Module,
    num_classes: int = 1000,
    init_weights: bool = True,
    dropout: float = 0.5,
) -> None:
    super().__init__()
    _log_api_usage_once(self)
    self.features = features
    if init_weights:
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)

extend_state_dict_input_channel 

extend_state_dict_input_channel(state_dict: Mapping[str, Any], input_weight_key: str, input_conv_layer: Conv2d) -> None

(Multipage) 3채널 이상인 이미지를 사용하기 위해 필요한 기능이며, state_dict의 input conv 채널이 네트워크의 input conv 채널보다 작은 경우 해당 weight의 채널을 늘려줍니다. 현재 네트워크가 가진 input conv 웨이트에서 앞 3 채널을 state_dict의 input conv 웨이트로 치환하는 방식을 사용합니다.

Parameters:

  • state_dict (Mapping[str, Any]) –

    로드하려는 weight

  • input_weight_key (str) –

    input conv weight의 이름

  • input_conv_layer (Conv2d) –

    input conv layer

Source code in SaigeToolkit/model/util.py 
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def extend_state_dict_input_channel(
    state_dict: Mapping[str, Any],
    input_weight_key: str,
    input_conv_layer: nn.Conv2d,
) -> None:
    """(Multipage) 3채널 이상인 이미지를 사용하기 위해 필요한 기능이며,
    state_dict의 input conv 채널이 네트워크의 input conv 채널보다 작은 경우 해당 weight의 채널을 늘려줍니다.
    현재 네트워크가 가진 input conv 웨이트에서 앞 3 채널을 state_dict의 input conv 웨이트로 치환하는 방식을 사용합니다.

    Args:
        state_dict (Mapping[str, Any]): 로드하려는 weight
        input_weight_key (str): input conv weight의 이름
        input_conv_layer (nn.Conv2d): input conv layer
    """
    state_dict_in_channels = state_dict[input_weight_key].shape[1]
    if state_dict_in_channels != input_conv_layer.in_channels:
        state_dict_in_channels = min(state_dict_in_channels, input_conv_layer.in_channels)
        original_input_conv_weight = input_conv_layer.state_dict()["weight"]
        original_input_conv_weight[:, :state_dict_in_channels] = state_dict[input_weight_key][
            :, :state_dict_in_channels
        ]
        state_dict[input_weight_key] = original_input_conv_weight

make_layers 

make_layers(cfg: List[Union[str, int]], batch_norm: bool = False, in_channels: int = 3) -> Sequential
Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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def make_layers(
    cfg: List[Union[str, int]], batch_norm: bool = False, in_channels: int = 3
) -> nn.Sequential:
    layers: List[nn.Module] = []
    for v in cfg:
        if v == "M":
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            v = cast(int, v)
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)

_vgg 

_vgg(cfg: str, batch_norm: bool, weights: Optional[WeightsEnum], progress: bool, in_channels: int = 3, **kwargs: Any) -> VGG
Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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def _vgg(
    cfg: str,
    batch_norm: bool,
    weights: Optional[WeightsEnum],
    progress: bool,
    in_channels: int = 3,
    **kwargs: Any,
) -> VGG:
    if weights is not None:
        kwargs["init_weights"] = False
        if weights.meta["categories"] is not None:
            _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
    model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm, in_channels=in_channels), **kwargs)
    if weights is not None:
        model.load_state_dict(weights.get_state_dict(progress=progress))
    return model

vgg11 

vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG

VGG-11 from Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>__.

Parameters:

  • weights –

    class:~torchvision.models.VGG11_Weights, optional): The pretrained weights to use. See :class:~torchvision.models.VGG11_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, default: True ) –

    If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs (Any, default: {} ) –

    parameters passed to the torchvision.models.vgg.VGG base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>_ for more details about this class.

.. autoclass:: torchvision.models.VGG11_Weights :members:

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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@handle_legacy_interface(weights=("pretrained", VGG11_Weights.IMAGENET1K_V1))
def vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    """VGG-11 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.

    Args:
        weights (:class:`~torchvision.models.VGG11_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.VGG11_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.VGG11_Weights
        :members:
    """
    weights = VGG11_Weights.verify(weights)

    return _vgg("A", False, weights, progress, **kwargs)

vgg11_bn 

vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG

VGG-11-BN from Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>__.

Parameters:

  • weights –

    class:~torchvision.models.VGG11_BN_Weights, optional): The pretrained weights to use. See :class:~torchvision.models.VGG11_BN_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, default: True ) –

    If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs (Any, default: {} ) –

    parameters passed to the torchvision.models.vgg.VGG base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>_ for more details about this class.

.. autoclass:: torchvision.models.VGG11_BN_Weights :members:

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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@handle_legacy_interface(weights=("pretrained", VGG11_BN_Weights.IMAGENET1K_V1))
def vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    """VGG-11-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.

    Args:
        weights (:class:`~torchvision.models.VGG11_BN_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.VGG11_BN_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.VGG11_BN_Weights
        :members:
    """
    weights = VGG11_BN_Weights.verify(weights)

    return _vgg("A", True, weights, progress, **kwargs)

vgg13 

vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG

VGG-13 from Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>__.

Parameters:

  • weights –

    class:~torchvision.models.VGG13_Weights, optional): The pretrained weights to use. See :class:~torchvision.models.VGG13_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, default: True ) –

    If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs (Any, default: {} ) –

    parameters passed to the torchvision.models.vgg.VGG base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>_ for more details about this class.

.. autoclass:: torchvision.models.VGG13_Weights :members:

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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@handle_legacy_interface(weights=("pretrained", VGG13_Weights.IMAGENET1K_V1))
def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    """VGG-13 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.

    Args:
        weights (:class:`~torchvision.models.VGG13_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.VGG13_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.VGG13_Weights
        :members:
    """
    weights = VGG13_Weights.verify(weights)

    return _vgg("B", False, weights, progress, **kwargs)

vgg13_bn 

vgg13_bn(*, weights: Optional[VGG13_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG

VGG-13-BN from Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>__.

Parameters:

  • weights –

    class:~torchvision.models.VGG13_BN_Weights, optional): The pretrained weights to use. See :class:~torchvision.models.VGG13_BN_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, default: True ) –

    If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs (Any, default: {} ) –

    parameters passed to the torchvision.models.vgg.VGG base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>_ for more details about this class.

.. autoclass:: torchvision.models.VGG13_BN_Weights :members:

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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@handle_legacy_interface(weights=("pretrained", VGG13_BN_Weights.IMAGENET1K_V1))
def vgg13_bn(*, weights: Optional[VGG13_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    """VGG-13-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.

    Args:
        weights (:class:`~torchvision.models.VGG13_BN_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.VGG13_BN_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.VGG13_BN_Weights
        :members:
    """
    weights = VGG13_BN_Weights.verify(weights)

    return _vgg("B", True, weights, progress, **kwargs)

vgg16 

vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG

VGG-16 from Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>__.

Parameters:

  • weights –

    class:~torchvision.models.VGG16_Weights, optional): The pretrained weights to use. See :class:~torchvision.models.VGG16_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, default: True ) –

    If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs (Any, default: {} ) –

    parameters passed to the torchvision.models.vgg.VGG base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>_ for more details about this class.

.. autoclass:: torchvision.models.VGG16_Weights :members:

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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@handle_legacy_interface(weights=("pretrained", VGG16_Weights.IMAGENET1K_V1))
def vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    """VGG-16 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.

    Args:
        weights (:class:`~torchvision.models.VGG16_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.VGG16_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.VGG16_Weights
        :members:
    """
    weights = VGG16_Weights.verify(weights)

    return _vgg("D", False, weights, progress, **kwargs)

vgg16_bn 

vgg16_bn(*, weights: Optional[VGG16_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG

VGG-16-BN from Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>__.

Parameters:

  • weights –

    class:~torchvision.models.VGG16_BN_Weights, optional): The pretrained weights to use. See :class:~torchvision.models.VGG16_BN_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, default: True ) –

    If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs (Any, default: {} ) –

    parameters passed to the torchvision.models.vgg.VGG base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>_ for more details about this class.

.. autoclass:: torchvision.models.VGG16_BN_Weights :members:

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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@handle_legacy_interface(weights=("pretrained", VGG16_BN_Weights.IMAGENET1K_V1))
def vgg16_bn(*, weights: Optional[VGG16_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    """VGG-16-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.

    Args:
        weights (:class:`~torchvision.models.VGG16_BN_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.VGG16_BN_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.VGG16_BN_Weights
        :members:
    """
    weights = VGG16_BN_Weights.verify(weights)

    return _vgg("D", True, weights, progress, **kwargs)

vgg19 

vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG

VGG-19 from Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>__.

Parameters:

  • weights –

    class:~torchvision.models.VGG19_Weights, optional): The pretrained weights to use. See :class:~torchvision.models.VGG19_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, default: True ) –

    If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs (Any, default: {} ) –

    parameters passed to the torchvision.models.vgg.VGG base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>_ for more details about this class.

.. autoclass:: torchvision.models.VGG19_Weights :members:

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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@handle_legacy_interface(weights=("pretrained", VGG19_Weights.IMAGENET1K_V1))
def vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    """VGG-19 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.

    Args:
        weights (:class:`~torchvision.models.VGG19_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.VGG19_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.VGG19_Weights
        :members:
    """
    weights = VGG19_Weights.verify(weights)

    return _vgg("E", False, weights, progress, **kwargs)

vgg19_bn 

vgg19_bn(*, weights: Optional[VGG19_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG

VGG-19_BN from Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>__.

Parameters:

  • weights –

    class:~torchvision.models.VGG19_BN_Weights, optional): The pretrained weights to use. See :class:~torchvision.models.VGG19_BN_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, default: True ) –

    If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs (Any, default: {} ) –

    parameters passed to the torchvision.models.vgg.VGG base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>_ for more details about this class.

.. autoclass:: torchvision.models.VGG19_BN_Weights :members:

Source code in SaigeToolkit/model/backbone/torchvision/vgg.py 
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@handle_legacy_interface(weights=("pretrained", VGG19_BN_Weights.IMAGENET1K_V1))
def vgg19_bn(*, weights: Optional[VGG19_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    """VGG-19_BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.

    Args:
        weights (:class:`~torchvision.models.VGG19_BN_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.VGG19_BN_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vgg.VGG``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.VGG19_BN_Weights
        :members:
    """
    weights = VGG19_BN_Weights.verify(weights)

    return _vgg("E", True, weights, progress, **kwargs)