
    2Vhc                     ,   d dl mZ d dlmZ d dlmZ  eddg      	 	 	 	 	 	 	 	 dd       Z edd	g      	 	 	 	 	 	 	 	 dd
       Z eddg      	 	 	 	 	 	 	 	 dd       Z ed      dd       Z ed      dd       Z	ej                  j                  dej                  ej                        e_        ej                  j                  e	_        dZ eedej                  ez           eedej                  ez           eedej                  ez          y)    )keras_export)imagenet_utils)resnetzkeras.applications.ResNet50V2z'keras.applications.resnet_v2.ResNet50V2Nc                 J    d }t        j                  |dd|d| ||||||      S )z)Instantiates the ResNet50V2 architecture.c                     t        j                  | ddd      } t        j                  | ddd      } t        j                  | dd	d
      } t        j                  | dddd      S )N@      conv2name      conv3      conv4      conv5stride1r   r   stack_residual_blocks_v2xs    P/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/applications/resnet_v2.pystack_fnzResNet50V2.<locals>.stack_fn   sb    ++Ar17C++AsAGD++AsAGD..sAqw
 	
    T
resnet50v2	r   weights_nameinclude_topweightsinput_tensorinput_shapepoolingclassesclassifier_activationr   ResNet	r"   r#   r$   r%   r&   r'   r(   r   r   s	            r   
ResNet50V2r,      s>    $
 ==!!3 r   zkeras.applications.ResNet101V2z(keras.applications.resnet_v2.ResNet101V2c                 J    d }t        j                  |dd|d| ||||||      S )z*Instantiates the ResNet101V2 architecture.c                     t        j                  | ddd      } t        j                  | ddd      } t        j                  | dd	d
      } t        j                  | dddd      S )Nr   r	   r
   r   r   r   r   r      r   r   r   r   r   r   r   s    r   r   zResNet101V2.<locals>.stack_fnB   b    ++Ar17C++AsAGD++AsBWE..sAqw
 	
r   Tresnet101v2r    r)   r+   s	            r   ResNet101V2r2   0   >    $
 =="!3 r   zkeras.applications.ResNet152V2z(keras.applications.resnet_v2.ResNet152V2c                 J    d }t        j                  |dd|d| ||||||      S )z*Instantiates the ResNet152V2 architecture.c                     t        j                  | ddd      } t        j                  | ddd      } t        j                  | dd	d
      } t        j                  | dddd      S )Nr   r	   r
   r   r      r   r   $   r   r   r   r   r   r   r   s    r   r   zResNet152V2.<locals>.stack_fnl   r0   r   Tresnet152v2r    r)   r+   s	            r   ResNet152V2r9   Z   r3   r   z-keras.applications.resnet_v2.preprocess_inputc                 2    t        j                  | |d      S )Ntf)data_formatmode)r   preprocess_input)r   r<   s     r   r>   r>      s    **	{ r   z/keras.applications.resnet_v2.decode_predictionsc                 0    t        j                  | |      S )N)top)r   decode_predictions)predsr@   s     r   rA   rA      s    ,,U<<r    )r=   reterrora	  

Reference:
- [Identity Mappings in Deep Residual Networks](
    https://arxiv.org/abs/1603.05027) (CVPR 2016)

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 ResNet, call `keras.applications.resnet_v2.preprocess_input` on your
inputs before passing them to the model. `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 `(224, 224, 3)`
        (with `"channels_last"` data format) or `(3, 224, 224)`
        (with `"channels_first"` data format). It should have exactly 3
        inputs 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"`.
    name: The name of the model (string).

Returns:
    A Model instance.
__doc__)TimagenetNNN  softmaxr   )TrG   NNNrH   rI   r1   )TrG   NNNrH   rI   r8   )N)   )keras.src.api_exportr   keras.src.applicationsr   r   r,   r2   r9   r>   rA   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOCrF   DOCsetattr r   r   <module>rT      s   - 1 ) '1 #	!!H (2 #	!!H (2 #	!!H => ? ?@= A= *>>EE	22

3
3 F   
 ,>>FF  4l 
Iz11C7 8 Y 3 3c 9 : Y 3 3c 9 :r   