
    &Vf1                     l   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d
dg          	 	 	 	 	 	 	 dd            Z ed          dd            Z ed          dd            Zej                            dej        ej                  e_        ej        j        e_        dS )    )backend)layers)keras_export)imagenet_utils)
Functional)operation_utils)
file_utilszthttps://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels.h5zzhttps://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5z$keras.applications.xception.Xceptionzkeras.applications.XceptionTimagenetN  softmaxc           
         |dv s#t          j        |          st          d          |dk    r| r|dk    rt          d|           t          j        |ddt          j                    | |          }|t          j        |
          }n-t          j	        |          st          j        ||          }n|}t          j                    dk    rdnd} t          j
        ddddd          |          }	 t          j        |d          |	          }	 t          j        dd          |	          }	 t          j
        dddd          |	          }	 t          j        |d          |	          }	 t          j        dd          |	          }	 t          j
        dd dd!d"          |	          }
 t          j        |#          |
          }
 t          j        ddd!dd$%          |	          }	 t          j        |d&          |	          }	 t          j        dd'          |	          }	 t          j        ddd!dd(%          |	          }	 t          j        |d)          |	          }	 t          j        ddd!d*+          |	          }	t          j        |	|
g          }	 t          j
        d,d dd!d"          |	          }
 t          j        |#          |
          }
 t          j        dd-          |	          }	 t          j        d,dd!dd.%          |	          }	 t          j        |d/          |	          }	 t          j        dd0          |	          }	 t          j        d,dd!dd1%          |	          }	 t          j        |d2          |	          }	 t          j        ddd!d3+          |	          }	t          j        |	|
g          }	 t          j
        d4d dd!d"          |	          }
 t          j        |#          |
          }
 t          j        dd5          |	          }	 t          j        d4dd!dd6%          |	          }	 t          j        |d7          |	          }	 t          j        dd8          |	          }	 t          j        d4dd!dd9%          |	          }	 t          j        |d:          |	          }	 t          j        ddd!d;+          |	          }	t          j        |	|
g          }	t!          d<          D ]k}|	}
d=t#          |d>z             z   } t          j        d|d?z             |	          }	 t          j        d4dd!d|d@z   %          |	          }	 t          j        ||dAz             |	          }	 t          j        d|dBz             |	          }	 t          j        d4dd!d|dCz   %          |	          }	 t          j        ||dDz             |	          }	 t          j        d|dEz             |	          }	 t          j        d4dd!d|dFz   %          |	          }	 t          j        ||dGz             |	          }	t          j        |	|
g          }	m t          j
        dHd dd!d"          |	          }
 t          j        |#          |
          }
 t          j        ddI          |	          }	 t          j        d4dd!ddJ%          |	          }	 t          j        |dK          |	          }	 t          j        ddL          |	          }	 t          j        dHdd!ddM%          |	          }	 t          j        |dN          |	          }	 t          j        ddd!dO+          |	          }	t          j        |	|
g          }	 t          j        dPdd!ddQ%          |	          }	 t          j        |dR          |	          }	 t          j        ddS          |	          }	 t          j        dTdd!ddU%          |	          }	 t          j        |dV          |	          }	 t          j        ddW          |	          }	| rT t          j        dX          |	          }	t          j        ||            t          j        ||dYZ          |	          }	nE|d[k    r t          j                    |	          }	n"|d\k    r t          j                    |	          }	|t-          j        |          }n|}t1          ||	d]          }|dk    rS| rt          j        d^t4          d_d`a          }nt          j        dbt6          d_dca          }|                    |           n||                    |           |S )da
  Instantiates the Xception architecture.

    Reference:
    - [Xception: Deep Learning with Depthwise Separable Convolutions](
        https://arxiv.org/abs/1610.02357) (CVPR 2017)

    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 image size for this model is 299x299.

    Note: each Keras Application expects a specific kind of input preprocessing.
    For Xception, call `keras.applications.xception.preprocess_input`
    on your inputs before passing them to the model.
    `xception.preprocess_input` will scale input pixels between -1 and 1.

    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 `(299, 299, 3)`.
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 71.
            E.g. `(150, 150, 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 model instance.
    >   Nr
   zThe `weights` argument should be either `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded.r
   r   zcIf using `weights='imagenet'` with `include_top=True`, `classes` should be 1000.  Received classes=i+  G   )default_sizemin_sizedata_formatrequire_flattenweightsN)shape)tensorr   channels_first       )   r   )   r   Fblock1_conv1)stridesuse_biasnameblock1_conv1_bn)axisr   relublock1_conv1_act)r   @   block1_conv2)r   r   block1_conv2_bnblock1_conv2_act   )r   r   same)r   paddingr   )r!   block2_sepconv1)r*   r   r   block2_sepconv1_bnblock2_sepconv2_actblock2_sepconv2block2_sepconv2_bnblock2_pool)r   r*   r      block3_sepconv1_actblock3_sepconv1block3_sepconv1_bnblock3_sepconv2_actblock3_sepconv2block3_sepconv2_bnblock3_pooli  block4_sepconv1_actblock4_sepconv1block4_sepconv1_bnblock4_sepconv2_actblock4_sepconv2block4_sepconv2_bnblock4_pool   block   _sepconv1_act	_sepconv1_sepconv1_bn_sepconv2_act	_sepconv2_sepconv2_bn_sepconv3_act	_sepconv3_sepconv3_bni   block13_sepconv1_actblock13_sepconv1block13_sepconv1_bnblock13_sepconv2_actblock13_sepconv2block13_sepconv2_bnblock13_pooli   block14_sepconv1block14_sepconv1_bnblock14_sepconv1_acti   block14_sepconv2block14_sepconv2_bnblock14_sepconv2_actavg_poolpredictions)
activationr   avgmaxxceptionz.xception_weights_tf_dim_ordering_tf_kernels.h5models 0a58e3b7378bc2990ea3b43d5981f1f6)cache_subdir	file_hashz4xception_weights_tf_dim_ordering_tf_kernels_notop.h5 b0042744bf5b25fce3cb969f33bebb97)r	   exists
ValueErrorr   obtain_input_shaper   image_data_formatr   Inputis_keras_tensorConv2DBatchNormalization
ActivationSeparableConv2DMaxPooling2DaddrangestrGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Dr   get_source_inputsr   get_fileWEIGHTS_PATHWEIGHTS_PATH_NO_TOPload_weights)include_topr   input_tensorinput_shapepoolingclassesclassifier_activation	img_inputchannel_axisxresidualiprefixinputsmodelweights_paths                   \/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/applications/xception.pyXceptionr      s   T )))Z->w-G-G)<
 
 	
 *D* '* *
 
 	
 !3-//#  K L{333		&|44 	%LLLLII$I1337GGG11RL	
FFU	 	 		 	A 	M!|:KLLLQOOA:&'9:::1==AFb&5~FFFqIIAL!|:KLLLQOOA:&'9:::1==Av}VVVe  	 	H <v(l;;;HEEH	VVe:K	 	 			 		A 	P!|:NOOO		 	A 	>&'<===a@@A	VVe:K	 	 			 		A 	P!|:NOOO		 	A	]	 	 			 		A 	
Ax=!!Av}VVVe  	 	H <v(l;;;HEEH=&'<===a@@A	VVe:K	 	 			 		A 	P!|:NOOO		 	A 	>&'<===a@@A	VVe:K	 	 			 		A 	P!|:NOOO		 	A	]	 	 			 		A 	
Ax=!!Av}VVVe  	 	H <v(l;;;HEEH=&'<===a@@A	VVe:K	 	 			 		A 	P!|:NOOO		 	A 	>&'<===a@@A	VVe:K	 	 			 		A 	P!|:NOOO		 	A	]	 	 			 		A 	
Ax=!!A1XX && &&3q1u::%DFf6O+CDDDQGG
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  
F%F^$;
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  EFf6O+CDDDQGG
F"+%
 
 
  
F%F^$;
 
 

  EFf6O+CDDDQGG
F"+%
 
 
  
F%F^$;
 
 

  J8}%%v}fffu  	 	H <v(l;;;HEEH>&'=>>>qAAA	VVe:L	 	 			 		A	! 5	 	 			 		A 	?&'=>>>qAAA	ffu;M	 	 			 		A	! 5	 	 			 		A	^	 	 			 		A 	
Ax=!!A	ffu;M	 	 			 		A	! 5	 	 			 		A 	?&'=>>>qAAA	ffu;M	 	 			 		A	! 5	 	 			 		A 	?&'=>>>qAAA 
/:F)z:::1==*+@'JJJ
FL 5M
 
 

  e/-//22AA+)++A..A  2<@@vqz222E * 	%.@%<	  LL &.F#%<	  L 	<((((		7###L    z,keras.applications.xception.preprocess_inputc                 0    t          j        | |d          S )Ntf)r   mode)r   preprocess_input)r   r   s     r   r   r   P  s#    *	{   r   z.keras.applications.xception.decode_predictionsrB   c                 .    t          j        | |          S )N)top)r   decode_predictions)predsr   s     r   r   r   W  s    ,U<<<<r    )r   reterror)Tr
   NNNr   r   )N)rB   )	keras.srcr   r   keras.src.api_exportr   keras.src.applicationsr   keras.src.modelsr   keras.src.opsr   keras.src.utilsr	   rx   ry   r   r   r   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOC__doc__ r   r   <module>r      s               - - - - - - 1 1 1 1 1 1 ' ' ' ' ' ' ) ) ) ) ) ) & & & & & &> 
D  .%  #t t t tn	 <==   >= >??= = = @?= *>EE	2

3 F    
 ,>F    r   