
    &Vf8                        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 dlmZ d	Z ed
dg          	 	 	 	 	 	 	 d!d            Z	 	 	 	 	 d"dZ G d de          Zd#dZ ed          d$d            Z ed          d%d            Zej                            dej        ej                   e_        ej        j        e_        dS )&    )backend)layers)keras_export)imagenet_utils)Layer)
Functional)operation_utils)
file_utilszQhttps://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/z8keras.applications.inception_resnet_v2.InceptionResNetV2z$keras.applications.InceptionResNetV2T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          |dddd          }t          |ddd          }t          |dd          } t          j        dd          |          }t          |ddd          }t          |ddd          } t          j        dd          |          }t          |dd          }	t          |dd          }
t          |
dd          }
t          |dd          }t          |dd          }t          |dd          } t          j        ddd          |          }t          |dd          }|	|
||g}t          j                    dk    rdnd} t          j        |d          |          }t          dd          D ]}t          |dd |!          }t          |d"ddd          }	t          |d#d          }
t          |
d#d          }
t          |
d"ddd          }
 t          j        ddd          |          }|	|
|g} t          j        |d$          |          }t          dd%          D ]}t          |d&d'|!          }t          |d#d          }	t          |	d"ddd          }	t          |d#d          }
t          |
d(ddd          }
t          |d#d          }t          |d(d          }t          |d)ddd          } t          j        ddd          |          }|	|
||g} t          j        |d*          |          }t          dd+          D ]}t          |d,d-|!          }t          |d.d	d-d+/          }t          |d0dd12          }| rT t          j        d32          |          }t          j        ||            t          j        ||d45          |          }nE|d6k    r t          j                    |          }n"|d7k    r t          j                    |          }|t)          j        |          }n|}t-          ||d82          }|dk    r]| r#d9}t          j        |t0          |z   d:d;<          }n"d=}t          j        |t0          |z   d:d><          }|                    |           n||                    |           |S )?a  Instantiates the Inception-ResNet v2 architecture.

    Reference:
    - [Inception-v4, Inception-ResNet and the Impact of
       Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
      (AAAI 2017)

    This function returns a Keras image classification model,
    optionally loaded with weights pre-trained on ImageNet.

    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/).

    Note: each Keras Application expects a specific kind of
    input preprocessing. For InceptionResNetV2, call
    `keras.applications.inception_resnet_v2.preprocess_input`
    on your inputs before passing them to the model.
    `inception_resnet_v2.preprocess_input`
    will scale input pixels between -1 and 1.

    Args:
        include_top: whether to include the fully-connected
            layer 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)`
            (with `'channels_last'` data format)
            or `(3, 299, 299)` (with `'channels_first'` data format).
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 75.
            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   zbIf using `weights="imagenet"` with `include_top=True`, `classes` should be 1000. Received classes=i+  K   )default_sizemin_sizedata_formatrequire_flattenweightsN)shape)tensorr             valid)stridespadding)r   @   )r   P         `   0      samechannels_firstmixed_5baxisname   g(\?block35)scale
block_type	block_idxi     mixed_6a   g?block17i   i@  mixed_7a
   g?block8g      ?)r,   
activationr-   r.   i   conv_7br)   avg_poolpredictions)r6   r)   avgmaxinception_resnet_v2z9inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5models e693bd0210a403b3192acc6073ad2e96)cache_subdir	file_hashz?inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5 d19885ff4a710c122648d3b5c3b684e4)r
   exists
ValueErrorr   obtain_input_shaper   image_data_formatr   Inputis_keras_tensor	conv2d_bnMaxPooling2DAveragePooling2DConcatenaterangeinception_resnet_blockGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Dr	   get_source_inputsr   get_fileBASE_WEIGHT_URLload_weights)include_topr   input_tensorinput_shapepoolingclassesclassifier_activation	img_inputxbranch_0branch_1branch_2branch_poolbrancheschannel_axisr.   inputsmodelfnameweights_paths                       g/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/applications/inception_resnet_v2.pyInceptionResNetV2rj      sz   ^ )))Z->w-G-G)<
 
 	
 *D* '* *
 
 	
 !3-//#  K L{333		&|44 	%LLLLII$I 	)RAw???A!RG,,,A!RA)Aq)))!,,A!RG,,,A!S!W---A)Aq)))!,,A B""HB""H2q))HB""H2q))H2q))HG&)!QGGGJJKKQ//K(Hk:H1337GGG11QL>:>>>xHHA 1b\\ 
 
	"Ti9
 
 

 CAw???HC##H3**H31gFFFHD&%aGDDDQGGK(K0H>:>>>xHHA 1b\\ 
 
	"SY)
 
 

 C##H31gFFFHC##H31gFFFHC##H3**H31gFFFHD&%aGDDDQGGK(Hk:H>:>>>xHHA 1b\\ 
 
	"SX
 
 
 		(b	 	 	A
 	!T19---A /:F)z:::1==*+@'JJJ
FL 5M
 
 

  e/-//22AA+)++A..A  2<@@ vq'<===E * 	OE%.%'%<	  LL6  &.%'%<	  L 	<((((		7###L    r   r$   reluFc           	      ,    t          j        ||||||          |           } |sDt          j                    dk    rdnd}|dn|dz   }	 t          j        |d|	          |           } |(|dn|d	z   }
 t          j        ||

          |           } | S )a2  Utility function to apply conv + BN.

    Args:
        x: input tensor.
        filters: filters in `Conv2D`.
        kernel_size: kernel size as in `Conv2D`.
        strides: strides in `Conv2D`.
        padding: padding mode in `Conv2D`.
        activation: activation in `Conv2D`.
        use_bias: whether to use a bias in `Conv2D`.
        name: name of the ops; will become `name + '_ac'`
            for the activation and `name + '_bn'` for the batch norm layer.

    Returns:
        Output tensor after applying `Conv2D` and `BatchNormalization`.
    )r   r   use_biasr)   r%   r   r   N_bnF)r(   r,   r)   _acr8   )r   Conv2Dr   rF   BatchNormalization
Activation)r^   filterskernel_sizer   r   r6   rn   r)   bn_axisbn_nameac_names              ri   rI   rI      s    4		 	 	 		 		A  
0226FFF!!A,$$D5LNF%7%gNNN
 
 ,$$D5L7Fjw777::Hrk   c                   .     e Zd Z fdZ fdZd Z xZS )CustomScaleLayerc                 H     t                      j        di | || _        d S )N )super__init__r,   )selfr,   kwargs	__class__s      ri   r~   zCustomScaleLayer.__init__%  s+    ""6"""


rk   c                     t                                                      }|                    d| j        i           |S )Nr,   )r}   
get_configupdater,   )r   configr   s     ri   r   zCustomScaleLayer.get_config)  s6    ##%%w
+,,,rk   c                 4    |d         |d         | j         z  z   S )Nr   r   )r,   )r   re   s     ri   callzCustomScaleLayer.call.  s    ay6!9tz111rk   )__name__
__module____qualname__r~   r   r   __classcell__)r   s   @ri   rz   rz   $  s`                
2 2 2 2 2 2 2rk   rz   c           	         |dk    rlt          | dd          }t          | dd          }t          |dd          }t          | dd          }t          |dd          }t          |dd          }|||g}n|dk    rMt          | dd          }t          | d	d          }t          |d
ddg          }t          |dddg          }||g}nr|dk    rMt          | dd          }t          | dd          }t          |dddg          }t          |dddg          }||g}nt          dt          |          z             |dz   t          |          z   }	t          j                    dk    rdnd}
 t          j        |
|	dz             |          }t          || j        |
         ddd|	dz             } t          |          | |g          } |" t          j	        ||	dz             |           } | S )a  Adds an Inception-ResNet block.

    Args:
        x: input tensor.
        scale: scaling factor to scale the residuals
            (i.e., the output of passing `x` through an inception module)
            before adding them to the shortcut
            branch. Let `r` be the output from the residual branch,
            the output of this block will be `x + scale * r`.
        block_type: `'block35'`, `'block17'` or `'block8'`,
            determines the network structure in the residual branch.
        block_idx: an `int` used for generating layer names.
            The Inception-ResNet blocks are repeated many times
            in this network. We use `block_idx` to identify each
            of the repetitions. For example, the first
            Inception-ResNet-A block will have
            `block_type='block35', block_idx=0`, and the layer names
            will have a common prefix `'block35_0'`.
        activation: activation function to use at the end of the block.

    Returns:
        Output tensor for the block.
    r+   r   r   r   r"   r   r2   r             r5      r/   zXUnknown Inception-ResNet block type. Expects "block35", "block17" or "block8", but got: _r%   _mixedr'   NT_conv)r6   rn   r)   rp   r8   )
rI   rD   strr   rF   r   rL   r   rz   rs   )r^   r,   r-   r.   r6   r_   r`   ra   rc   
block_namerd   mixedups                ri   rN   rN   2  sL   0 YQA&&QA&&Xr1--QA&&Xr1--Xr1--h1	y	 	 QQ''QQ''XsQF33XsQF33h'	x		QQ''QQ''XsQF33XsQF33h'j//*
 
 	
 c!C	NN2J1337GGG11QLMFLzH7LMMM E 
		'!
 
 
B 	 B((ABFjzE/ABBB1EEHrk   z7keras.applications.inception_resnet_v2.preprocess_inputc                 0    t          j        | |d          S )Ntf)r   mode)r   preprocess_input)r^   r   s     ri   r   r   y  s#    *	{   rk   z9keras.applications.inception_resnet_v2.decode_predictionsr#   c                 .    t          j        | |          S )N)top)r   decode_predictions)predsr   s     ri   r   r     s    ,U<<<<rk    )r   reterror)Tr   NNNr   r   )r   r$   rl   FN)rl   )N)r#   )	keras.srcr   r   keras.src.api_exportr   keras.src.applicationsr   keras.src.layers.layerr   keras.src.modelsr   keras.src.opsr	   keras.src.utilsr
   rU   rj   rI   rz   rN   r   r   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOC__doc__r|   rk   ri   <module>r      s               - - - - - - 1 1 1 1 1 1 ( ( ( ( ( ( ' ' ' ' ' ' ) ) ) ) ) ) & & & & & &.  B.  #] ] ] ]H 	+ + + +\2 2 2 2 2u 2 2 2D D D DN GHH   IH IJJ= = = KJ= *>EE	2

3 F    
 ,>F    rk   