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 ddlmZ ddlmZ ddlmZ dd	lmZ d
Zdaeddd$ddZd%ddZ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 ))zInception-ResNet V2 model for Keras.

Reference:
  - [Inception-v4, Inception-ResNet and the Impact of
     Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
    (AAAI 2017)
    N)backend)imagenet_utils)training)VersionAwareLayers)
data_utils)layer_utils)keras_exportzQhttps://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/z8keras.applications.inception_resnet_v2.InceptionResNetV2z$keras.applications.InceptionResNetV2Timagenet  softmaxc                 K   sH  d|v r| dant a|r,td|f |dv sJtjj|sJtd|dkrf| rf|dkrftdtj	|dd	t
 | |d
}|du rtj|d}nt
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t|	d%d}t|d*dddd}t|	d%d}t|d*d}t|d+dddd}tjdddd|	}|
|||g}tj|d,d|}	tdd-D ]}t|	d.d/|d#}	qt|	d0dd/d-d1}	t|	d2dd3d4}	| rtjd5d4|	}	t|| tj||d6d7|	}	n.|d8krt |	}	n|d9krt |	}	|durt|}n|}tj||	d:d4}|dkr0| r
d;}tj|t| d<d=d>}nd?}tj|t| d<d@d>}|| n|durD|| |S )Aa]  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
    `tf.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"`.
      **kwargs: For backwards compatibility only.

    Returns:
      A `keras.Model` instance.
    layerszUnknown argument(s): %s>   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
   zWIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000i+  K   )default_sizemin_sizedata_formatrequire_flattenweightsN)shape)tensorr             valid)stridespadding)r   @   )r   P         `   0      samechannels_firstZmixed_5baxisname   g(\?block35)scale
block_type	block_idxi     Zmixed_6a   g?block17i   i@  Zmixed_7a
   g?block8g      ?)r)   
activationr*   r+   i   Zconv_7br&   avg_poolpredictions)r1   r&   avgmaxinception_resnet_v2z9inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5modelsZ e693bd0210a403b3192acc6073ad2e96)cache_subdir	file_hashz?inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5Z d19885ff4a710c122648d3b5c3b684e4) popr   r   
ValueErrortfiogfileexistsr   obtain_input_shaper   image_data_formatInputis_keras_tensor	conv2d_bnMaxPooling2DAveragePooling2DConcatenaterangeinception_resnet_blockGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Dr   get_source_inputsr   Modelr   get_fileBASE_WEIGHT_URLload_weights)include_topr   input_tensorinput_shapepoolingclassesclassifier_activationkwargs	img_inputxbranch_0branch_1branch_2Zbranch_poolbrancheschannel_axisr+   inputsmodelfnameweights_path rf   b/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/applications/inception_resnet_v2.pyInceptionResNetV2+   s    M	











rh   r   r"   reluFc                 C   s   t j||||||d| } |sZt dkr.dnd}|du r>dn|d }	t j|d|	d| } |dur|du rndn|d	 }
t j||
d
| } | S )a  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&   _acr2   )r   Conv2Dr   rB   BatchNormalization
Activation)r\   filterskernel_sizer   r   r1   rj   r&   bn_axisbn_nameZac_namerf   rf   rg   rE     s(    rE   c                 C   s  |dkr\t | dd}t | dd}t |dd}t | dd}t |dd}t |dd}|||g}n|dkrt | dd}t | d	d}t |d
ddg}t |dddg}||g}nZ|dkrt | dd}t | dd}t |dddg}t |dddg}||g}ntdt| |d t| }	t dkr"dnd}
tj|
|	d d|}t |t| |
 ddd|	d d}tjdd t| dd d|i|	d| |g} |durtj	||	d d| } | S )a(  Adds an Inception-ResNet block.

    This function builds 3 types of Inception-ResNet blocks mentioned
    in the paper, controlled by the `block_type` argument (which is the
    block name used in the official TF-slim implementation):
    - Inception-ResNet-A: `block_type='block35'`
    - Inception-ResNet-B: `block_type='block17'`
    - Inception-ResNet-C: `block_type='block8'`

    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 (see
        [activations](../activations.md)). When `activation=None`, no activation
        is applied
        (i.e., "linear" activation: `a(x) = x`).

    Returns:
        Output tensor for the block.

    Raises:
      ValueError: if `block_type` is not one of `'block35'`,
        `'block17'` or `'block8'`.
    r(   r   r   r   r    r   r.   r            r0      r,   zXUnknown Inception-ResNet block type. Expects "block35", "block17" or "block8", but got: _r#   Z_mixedr$   NT_conv)r1   rj   r&   c                 S   s   | d | d |  S )Nr   r   rf   )rb   r)   rf   rf   rg   <lambda>      z(inception_resnet_block.<locals>.<lambda>r)   )output_shape	argumentsr&   rl   r2   )
rE   r<   strr   rB   r   rH   	int_shapeLambdaro   )r\   r)   r*   r+   r1   r]   r^   r_   r`   
block_namera   mixeduprf   rf   rg   rJ   B  sb    #

	
rJ   z7keras.applications.inception_resnet_v2.preprocess_inputc                 C   s   t j| |ddS )Nr=   )r   mode)r   preprocess_input)r\   r   rf   rf   rg   r     s    r   z9keras.applications.inception_resnet_v2.decode_predictionsr!   c                 C   s   t j| |dS )N)top)r   decode_predictions)predsr   rf   rf   rg   r     s    r    )r   reterror)Tr	   NNNr
   r   )r   r"   ri   FN)ri   )N)r!   )__doc__Ztensorflow.compat.v2compatv2r=   kerasr   Zkeras.applicationsr   keras.enginer   keras.layersr   keras.utilsr   r    tensorflow.python.util.tf_exportr   rR   r   rh   rE   rJ   r   r   PREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOCrf   rf   rf   rg   <module>   sR           j     
.
W