o
    ={cM'                     @   s   d Z ddlm  mZ ddlmZ ddlmZ ddl	m
Z
 ddlmZ ddlmZ ddlmZ dd	lmZ d
ZdZe Zedd							dddZeddddZeddddZejjdejejde_ ejj e_ dS ) zVGG16 model for Keras.

Reference:
  - [Very Deep Convolutional Networks for Large-Scale Image Recognition]
    (https://arxiv.org/abs/1409.1556) (ICLR 2015)
    N)backend)imagenet_utils)training)VersionAwareLayers)
data_utils)layer_utils)keras_exportznhttps://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5zthttps://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5zkeras.applications.vgg16.VGG16zkeras.applications.VGG16Timagenet  softmaxc                 C   sF  |dv st jj|std| |dkr$| r$|dkr$td| dtj|ddt | |d	}|d
u r<t	j
|d}nt|sIt	j
||d}n|}t	jdddddd|}t	jdddddd|}t	jdddd|}t	jdddddd|}t	jdddddd|}t	jdddd|}t	jdddddd|}t	jdddddd|}t	jdddddd|}t	jdddd|}t	jd dddd!d|}t	jd dddd"d|}t	jd dddd#d|}t	jddd$d|}t	jd dddd%d|}t	jd dddd&d|}t	jd dddd'd|}t	jddd(d|}| rIt	jd)d*|}t	jd+dd,d-|}t	jd+dd.d-|}t|| t	j||d/d-|}n|d0krUt	 |}n|d1kr`t	 |}|d
urkt|}	n|}	tj|	|d2d*}
|dkr| rtjd3td4d5d6}n	tjd7td4d8d6}|
| |
S |d
ur|
| |
S )9a  Instantiates the VGG16 model.

    Reference:
    - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
    https://arxiv.org/abs/1409.1556) (ICLR 2015)

    For image classification use cases, see
    [this page for detailed examples](
      https://keras.io/api/applications/#usage-examples-for-image-classification-models).

    For transfer learning use cases, make sure to read the
    [guide to transfer learning & fine-tuning](
      https://keras.io/guides/transfer_learning/).

    The default input size for this model is 224x224.

    Note: each Keras Application expects a specific kind of input preprocessing.
    For VGG16, call `tf.keras.applications.vgg16.preprocess_input` on your
    inputs before passing them to the model.
    `vgg16.preprocess_input` will convert the input images from RGB to BGR,
    then will zero-center each color channel with respect to the ImageNet
    dataset, without scaling.

    Args:
        include_top: whether to include the 3 fully-connected
            layers at the top of the network.
        weights: one of `None` (random initialization),
              'imagenet' (pre-training on ImageNet),
              or the path to the weights file to be loaded.
        input_tensor: optional Keras tensor
            (i.e. output of `layers.Input()`)
            to use as image input for the model.
        input_shape: optional shape tuple, only to be specified
            if `include_top` is False (otherwise the input shape
            has to be `(224, 224, 3)`
            (with `channels_last` data format)
            or `(3, 224, 224)` (with `channels_first` data format).
            It should have exactly 3 input channels,
            and width and height should be no smaller than 32.
            E.g. `(200, 200, 3)` would be one valid value.
        pooling: Optional pooling mode for feature extraction
            when `include_top` is `False`.
            - `None` means that the output of the model will be
                the 4D tensor output of the
                last convolutional block.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional block, and thus
                the output of the model will be a 2D tensor.
            - `max` means that global max pooling will
                be applied.
        classes: optional number of classes to classify images
            into, only to be specified if `include_top` is True, and
            if no `weights` argument is specified.
        classifier_activation: A `str` or callable. The activation function to
            use on the "top" layer. Ignored unless `include_top=True`. Set
            `classifier_activation=None` to return the logits of the "top"
            layer.  When loading pretrained weights, `classifier_activation` can
            only be `None` or `"softmax"`.

    Returns:
      A `keras.Model` instance.
    >   r	   NzThe `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.  Received: weights=r	   r
   zlIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000.  Received `classes=`       )Zdefault_sizeZmin_sizedata_formatZrequire_flattenweightsN)shape)Ztensorr   @   )   r   ZreluZsameZblock1_conv1)
activationZpaddingnameZblock1_conv2)   r   Zblock1_pool)stridesr      Zblock2_conv1Zblock2_conv2Zblock2_pool   Zblock3_conv1Zblock3_conv2Zblock3_conv3Zblock3_pooli   Zblock4_conv1Zblock4_conv2Zblock4_conv3Zblock4_poolZblock5_conv1Zblock5_conv2Zblock5_conv3Zblock5_poolZflatten)r   i   Zfc1)r   r   Zfc2ZpredictionsZavgmaxZvgg16z+vgg16_weights_tf_dim_ordering_tf_kernels.h5ZmodelsZ 64373286793e3c8b2b4e3219cbf3544b)Zcache_subdirZ	file_hashz1vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5Z 6d6bbae143d832006294945121d1f1fc)tfioZgfileexists
ValueErrorr   Zobtain_input_shaper   Zimage_data_formatlayersZInputZis_keras_tensorZConv2DZMaxPooling2DZFlattenZDenseZvalidate_activationZGlobalAveragePooling2DZGlobalMaxPooling2Dr   Zget_source_inputsr   ZModelr   Zget_fileWEIGHTS_PATHWEIGHTS_PATH_NO_TOPZload_weights)Zinclude_topr   Zinput_tensorZinput_shapeZpoolingZclassesZclassifier_activationZ	img_inputxZinputsZmodelZweights_path r#   8lib/python3.10/site-packages/keras/applications/vgg16.pyVGG160   s  I	




















r%   z)keras.applications.vgg16.preprocess_inputc                 C   s   t j| |ddS )NZcaffe)r   mode)r   preprocess_input)r"   r   r#   r#   r$   r'      s   r'   z+keras.applications.vgg16.decode_predictions   c                 C   s   t j| |dS )N)top)r   decode_predictions)Zpredsr)   r#   r#   r$   r*     s   r*    )r&   reterror)Tr	   NNNr
   r   )N)r(   )__doc__Ztensorflow.compat.v2compatZv2r   Zkerasr   Zkeras.applicationsr   Zkeras.enginer   Zkeras.layersr   Zkeras.utilsr   r   Z tensorflow.python.util.tf_exportr   r    r!   r   r%   r'   r*   ZPREPROCESS_INPUT_DOCformatZPREPROCESS_INPUT_RET_DOC_CAFFEZPREPROCESS_INPUT_ERROR_DOCr#   r#   r#   r$   <module>   sD    O