&VfCddlZddlmZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z d Zed d g ddZd dZ d!dZedd"dZedd#dZejdejeje_ejje_dS)$N)backend)layers) keras_export)imagenet_utils) Functional)operation_utils) file_utilszGhttps://storage.googleapis.com/tensorflow/keras-applications/mobilenet/z&keras.applications.mobilenet.MobileNetzkeras.applications.MobileNet?MbP?Timagenetsoftmaxc |dvs&tj|std||dkr|r|dkrtd||d} nGtjdkr|d } |d } n|d } |d } | | kr| d vr| } nd} t j|| d tj||}tjdkrd\} }nd\} }|| } ||} |dkrP|d krtd||dvrtd|| | ks| d vrd} tjdd |tj |}n-tj |stj ||}n|}t|d |d}t|d||d }t|d||dd }t|d||d}t|d ||dd!}t|d ||d"}t|d#||dd$}t|d#||d%}t|d#||d&}t|d#||d'}t|d#||d(}t|d#||d)}t|d*||dd+}t|d*||d,}|rtjd-.|}tj|d/0|}tj|d1d2d34|}tj|fd50|}t j| |tj| d67|}nE|d8krtj|}n"|d9krtj|}|t+j|}n|}t/||d:|d;d<| 0}|dkr|d=krd>}n|d?krd@}n |dAkrdB}ndC}|r)dD|| fz}t0|z}tj||dEF}n(dG|| fz}t0|z}tj||dEF}||n||||S)HaHInstantiates the MobileNet architecture. Reference: - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications]( https://arxiv.org/abs/1704.04861) 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 MobileNet, call `keras.applications.mobilenet.preprocess_input` on your inputs before passing them to the model. `mobilenet.preprocess_input` will scale input pixels between -1 and 1. Args: 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. Defaults to `None`. `input_shape` will be ignored if the `input_tensor` is provided. alpha: Controls the width of the network. This is known as the width multiplier in the MobileNet paper. - If `alpha < 1.0`, proportionally decreases the number of filters in each layer. - If `alpha > 1.0`, proportionally increases the number of filters in each layer. - If `alpha == 1`, default number of filters from the paper are used at each layer. Defaults to `1.0`. depth_multiplier: Depth multiplier for depthwise convolution. This is called the resolution multiplier in the MobileNet paper. Defaults to `1.0`. dropout: Dropout rate. Defaults to `0.001`. include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to `True`. weights: One of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to `"imagenet"`. input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. `input_tensor` is useful for sharing inputs between multiple different networks. Defaults to `None`. pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` (default) 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. Defaults to `1000`. 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. Received weights=r rzcIf using `weights='imagenet'` with `include_top=True`, `classes` should be 1000. Received classes=Nchannels_firstr r)r ) default_sizemin_size data_formatrequire_flattenweights channels_lastrr )r rz]If imagenet weights are being loaded, depth multiplier must be 1. Received depth_multiplier=)g???r zoIf imagenet weights are being loaded, alpha can be one of`0.25`, `0.50`, `0.75` or `1.0` only. Received alpha=z`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.) stacklevel)shape)tensorr")rr)strides@)block_idr)r$r&i i T)keepdimsdropoutnamer r same conv_preds)paddingr6 reshape_2 predictions) activationr6avgmax mobilenet_z0.2f_r 1_0r 7_5r5_02_5zmobilenet_%s_%d_tf.h5models) cache_subdirzmobilenet_%s_%d_tf_no_top.h5)r exists ValueErrorrimage_data_formatrobtain_input_shapewarningswarnrInputis_keras_tensor _conv_block_depthwise_conv_blockGlobalAveragePooling2DDropoutConv2DReshapevalidate_activation ActivationGlobalMaxPooling2Drget_source_inputsrBASE_WEIGHT_PATHget_file load_weights) input_shapealphadepth_multiplierr4 include_topr input_tensorpoolingclassesclassifier_activationrrowscolsrow_axiscol_axis img_inputxinputsmodel alpha_text model_name weight_path weights_paths ]/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/applications/mobilenet.py MobileNetrrs0x ) ) )Z->w-G-G ) *!(  * *   *D * ' * *     $ & &*: : :q>Dq>DDq>Dq>D 4<LLLA / 8F )4 8 8 8 ; ; 3FN7 3 3 3A 6 6 MFM'66 M M Ma P P 8FNG:K 8 8 8 ; ;*+@'JJJ F ,=      e  /-//22AA   +)++A..A 2<@@ vq'GE'G'G'G'G'G H H HE* C<<JJ d]]JJ d]]JJJ  0J3EEJ*Z7K%.KhLL8:t:LLJ*Z7K%.KhL <((((   7### Lr'r'r7c tjdkrdnd}t||z}tj||dd|d|}tj|d |}tjd d |S) aDAdds an initial convolution layer (with batch normalization and relu6). Args: inputs: Input tensor of shape `(rows, cols, 3)` (with `channels_last` data format) or (3, rows, cols) (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(224, 224, 3)` would be one valid value. filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. kernel: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. Input shape: 4D tensor with shape: `(samples, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, rows, cols, channels)` if data_format='channels_last'. # Output shape 4D tensor with shape: `(samples, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to stride. Returns: Output tensor of block. rr r8Fconv1r:use_biasr$r6conv1_bnaxisr6@ conv1_relur5)rrJintrrTBatchNormalizationReLU)rkfiltersr^kernelr$ channel_axisrjs rqrPrPsT 1337GGG11RL'E/""G          A F!|*EEEaHHA .6;s . . .q 1 11rsc jtjdkrdnd}t||z}|dkr|}n"tjdd|z|}tjd|dkrd nd ||d d |z |}tj|d|z|}tjdd|z|}tj|dd d dd|z|}tj|d|z|}tjdd|z|S)aAdds a depthwise convolution block. A depthwise convolution block consists of a depthwise conv, batch normalization, relu6, pointwise convolution, batch normalization and relu6 activation. Args: inputs: Input tensor of shape `(rows, cols, channels)` (with `channels_last` data format) or (channels, rows, cols) (with `channels_first` data format). pointwise_conv_filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the pointwise convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. block_id: Integer, a unique identification designating the block number. Input shape: 4D tensor with shape: `(batch, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, rows, cols, channels)` if data_format='channels_last'. # Output shape 4D tensor with shape: `(batch, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to stride. Returns: Output tensor of block. rr rvr7)rrz conv_pad_%dr5rtr8validFz conv_dw_%d)r:r_r$ryr6z conv_dw_%d_bnr{r}zconv_dw_%d_reluz conv_pw_%drxz conv_pw_%d_bnzconv_pw_%d_relu) rrJrr ZeroPadding2DDepthwiseConv2DrrrT)rkpointwise_conv_filtersr^r_r$r&rrjs rqrQrQHsd 1337GGG11RL !7%!?@@&  F =8#;      !V++) H $      A ! ( :     A < C/(:;;;A>>A   H $      A ! ( :     A ?6;s!2X!= > > >q A AArsz-keras.applications.mobilenet.preprocess_inputc0tj||dS)Ntf)rmode)rpreprocess_input)rjrs rqrrs#  * {   rsz/keras.applications.mobilenet.decode_predictionsr*c.tj||S)N)top)rdecode_predictions)predsrs rqrrs  ,U < < <rs------111111''''''))))))&&&&&&N 0&     #www  wt52525252x   SBSBSBSBl=>>?> ?@@===A@=*>EE 2  3F ,>Frs