o 2hx@sfddlZddlmZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z d Zed Zed Zed Zed Z              d5ddZeddg        d6ddZeddg        d7d d!Z d8d$d%Zd9d&d'Zd9d(d)Zd9d*d+Zed,d9d-d.Zed/d:d1d2Zejjd3ejejd4e_ ejj e_ dS);N)backend)layers) keras_export)imagenet_utils) Functional)operation_utils) file_utilszDhttps://storage.googleapis.com/tensorflow/keras-applications/nasnet/zNASNet-mobile.h5zNASNet-mobile-no-top.h5zNASNet-large.h5zNASNet-large-no-top.h5`TimagenetsoftmaxNASNetc Cstdkr td|dvst|std|dkr%|r%| dkr%tdt|tr 1.0, proportionally increases the number of filters in each layer. - If `filter_multiplier` = 1, default number of filters from the paper are used at each layer. include_top: Whether to include the fully-connected layer at the top of the network. weights: `None` (random initialization) or `imagenet` (ImageNet weights) input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 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. default_size: Specifies the default image size of the model 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. channels_firstzNASNet does not support the `channels_first` image data format. Switch to `channels_last` by editing your local config file at ~/.keras/keras.json>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 rzWIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000NzWhen specifying the input shape of a NASNet and loading `ImageNet` weights, the input_shape argument must be static (no None entries). Got: `input_shape=z`.K ) default_sizemin_size data_formatrequire_flattenweights channels_lastaThe NASNet family of models is only available for the input data format "channels_last" (width, height, channels). However your settings specify the default data format "channels_first" (channels, width, height). You should set `image_data_format="channels_last"` in your Keras config located at ~/.keras/keras.json. The model being returned right now will expect inputs to follow the "channels_last" data format.r ) stacklevel)shape)tensorrrzuFor NASNet-A models, the `penultimate_filters` must be a multiple of 24 * (`filter_multiplier` ** 2). Current value: r!r r validF stem_conv1 he_normal)stridespaddinguse_biasnamekernel_initializerZڊ?MbP?stem_bn1axismomentumepsilonr)stem_1block_idstem_2reduce_relu predictions) activationr)avgmaxr)znasnet_mobile.h5models 020fb642bf7360b370c678b08e0adf61) cache_subdir file_hashznasnet_mobile_no_top.h5 1ed92395b5b598bdda52abe5c0dbfd63znasnet_large.h5 11577c9a518f0070763c2b964a382f17znasnet_large_no_top.h5 d81d89dc07e6e56530c4e77faddd61b5zDImageNet weights can only be loaded with NASNetLarge or NASNetMobile)"rimage_data_format ValueErrorrexists isinstancetuplestrrobtain_input_shapewarningswarnrInputis_keras_tensorConv2DBatchNormalization_reduction_a_cellrange_normal_a_cell ActivationGlobalAveragePooling2Dvalidate_activationDenseGlobalMaxPooling2Drget_source_inputsrget_fileNASNET_MOBILE_WEIGHT_PATH NASNET_MOBILE_WEIGHT_PATH_NO_TOP load_weightsNASNET_LARGE_WEIGHT_PATHNASNET_LARGE_WEIGHT_PATH_NO_TOP) input_shapepenultimate_filters num_blocksstem_block_filtersskip_reductionfilter_multiplier include_topr input_tensorpoolingclassesrclassifier_activationr) img_input channel_dimfiltersxpip0inputsmodel weights_pathrvX/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/applications/nasnet.pyrs: ^                                z&keras.applications.nasnet.NASNetMobilezkeras.applications.NASNetMobile nasnet_mobilecCsHtdkr td|s|durd}t|ddddd |||||d ||d S) a Instantiates a Mobile NASNet model in ImageNet mode. Reference: - [Learning Transferable Architectures for Scalable Image Recognition]( https://arxiv.org/abs/1707.07012) (CVPR 2018) Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at `~/.keras/keras.json`. Note: each Keras Application expects a specific kind of input preprocessing. For NASNet, call `keras.applications.nasnet.preprocess_input` on your inputs before passing them to the model. 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)` for NASNetMobile 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. include_top: Whether to include the fully-connected layer at the top of the network. weights: `None` (random initialization) or `imagenet` (ImageNet weights). For loading `imagenet` weights, `input_shape` should be (224, 224, 3) input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 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 layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, 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 Keras model instance. torchz}NASNetMobile is not available with the torch backend at this time due to an outstanding bug. If interested, please open a PR.N)r=r=r!i rFr r= rbrcrdrerfrgrrhrirjrrkr))rrFrrargrrhrirjrkr)rvrvrw NASNetMobile;s, F r}z%keras.applications.nasnet.NASNetLargezkeras.applications.NASNetLarge nasnet_largecCs$t|ddddd|||||d||dS)a Instantiates a NASNet model in ImageNet mode. Reference: - [Learning Transferable Architectures for Scalable Image Recognition]( https://arxiv.org/abs/1707.07012) (CVPR 2018) Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at `~/.keras/keras.json`. Note: each Keras Application expects a specific kind of input preprocessing. For NASNet, call `keras.applications.nasnet.preprocess_input` on your inputs before passing them to the model. Args: input_shape: Optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(331, 331, 3)` for NASNetLarge. 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. include_top: Whether to include the fully-connected layer at the top of the network. weights: `None` (random initialization) or `imagenet` (ImageNet weights). For loading `imagenet` weights, `input_shape` should be (331, 331, 3) input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. 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 layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, 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 Keras model instance. r r r Tr rr{)rr|rvrvrw NASNetLarges Frr rrc Cstdkrdnd}td|ktd|}|dkr1tjt||d|d|}d }nd }tj|||d ||d d |}tj |ddd|d|}td|}tj||d|d d d|}tj |ddd|d|}Wd|S1swY|S)aFAdds 2 blocks of [relu-separable conv-batchnorm]. Args: ip: Input tensor filters: Number of output filters per layer kernel_size: Kernel size of separable convolutions strides: Strided convolution for downsampling block_id: String block_id Returns: A Keras tensor rrrseparable_conv_block_r7r"separable_conv_1_pad_r'r)r#sameseparable_conv_1_F)r&r)r'r(r+r,separable_conv_1_bn_r.separable_conv_2_)r)r'r(separable_conv_2_bn_N) rrE name_scoperrU ZeroPadding2Dr correct_padSeparableConv2DrQ)iprn kernel_sizer&r4rmroconv_padrvrvrw_separable_conv_blocksh  &&rc Csbtdkrdnd}tdkrdnd}td |dur"|}n|j||j|krtd|{tjd d |d |}tjd d dd|d|}tj|dd ddd|dd|}tjdd|}tj dd|}tjd d dd|d|}tj|dd ddd|dd|}tj ||g|d}tj |ddd|d |}Wdn1swYn^|j||krtd!|.td |}tj|d d dd"|ddd#|}tj |ddd|d |}Wdn(1swYWd|SWd|SWd|SWd|SWd|S1s*wY|S)$aAdjusts the input `previous path` to match the shape of the `input`. Used in situations where the output number of filters needs to be changed. Args: p: Input tensor which needs to be modified ip: Input tensor whose shape needs to be matched filters: Number of output filters to be matched block_id: String block_id Returns: Adjusted Keras tensor rrrr adjust_blockNadjust_reduction_block_r7adjust_relu_1_r<rr"r#adjust_avg_pool_1_r&r'r)rFadjust_conv_1_r%)r'r(r)r*)rrr)r')rrr)croppingadjust_avg_pool_2_adjust_conv_2_)r/r+r, adjust_bn_r.adjust_projection_block_adjust_conv_projection_r&r'r)r(r*) rrErrrrUAveragePooling2DrPr Cropping2D concatenaterQ)rprrnr4rmimg_dimp1p2rvrvrw _adjust_block-s  ,  C C C C CCrc Cstdkrdnd}td|Ot||||}td|}tj|dddd|d d d |}tj|d d d|d|}td)t||dd|d}t||d|d}tj ||gd|d}Wdn1srwYtd*t||dd|d} t||dd|d} tj | | gd|d} Wdn1swYtd"tj dddd|d |} tj | |gd!|d} Wdn1swYtd"0tj dddd#|d |} tj dddd$|d |}tj | |gd%|d}Wdn 1swYtd&t||d'|d}tj ||gd(|d}Wdn 1sBwYtj ||| | ||g|d)|d*}Wd||fS1sfwY||fS)+zAdds a Normal cell for NASNet-A (Fig. 4 in the paper). Args: ip: Input tensor `x` p: Input tensor `p` filters: Number of output filters block_id: String block_id Returns: A Keras tensor rrrnormal_A_block_r7rrnormal_conv_1_Fr%rr+r, normal_bn_1_r.block_1r normal_left1_)rr4normal_right1_r3 normal_add_1_r<Nblock_2 normal_left2_r normal_right2_ normal_add_2_block_3 normal_left3_r normal_add_3_block_4 normal_left4_normal_right4_ normal_add_4_block_5 normal_left5_ normal_add_5_normal_concat_r/r)) rrErrrrUrPrQraddrr)rrprnr4rmhx1_1x1_2x1x2_1x2_2x2x3x4_1x4_2x4x5rorvrvrwrTs          LLrTc Cstdkrdnd}td|mt||||}td|}tj|dddd|d d d |}tj|d d d|d|}tjt |dd|d|}td,t ||ddd|d}t ||ddd|d}tj ||gd|d} Wdn1swYtd.tj dddd |d!|} t ||ddd"|d} tj | | gd#|d} Wdn1swYtd$.tjdddd%|d!|} t ||ddd&|d}tj | |gd'|d}Wdn1swYtd(tjdddd)|d!| }t | |g}Wdn 1s&wYtd*-t | |dd)|d+}tj dddd,|d!|}tj ||gd-|d}Wdn 1sawYtj| |||g|d.|d/}||fWdS1swYdS)0zAdds a Reduction cell for NASNet-A (Fig. 4 in the paper). Args: ip: Input tensor `x` p: Input tensor `p` filters: Number of output filters block_id: String block_id Returns: A Keras tensor rrrreduction_A_block_r7rrreduction_conv_1_Fr%rr+r,reduction_bn_1_r.r!reduction_pad_1_rrrr"reduction_left1_)r&r4)rreduction_right1_reduction_add_1_r<Nrr r#reduction_left2_rreduction_right2_reduction_add_2_rreduction_left3_reduction_right3_reduction_add3_rreduction_left4_rr3reduction_right5_reduction_add4_reduction_concat_r)rrErrrrUrPrQrrrrr MaxPooling2Drr)rrprnr4rmrh3rrrrrrx3_1x3_2rrx5_1x5_2rrorvrvrwrRs         &rRz*keras.applications.nasnet.preprocess_inputcCstj||ddS)Ntf)rmode)rpreprocess_input)rorrvrvrwrUsrz,keras.applications.nasnet.decode_predictionsrcCstj||dS)N)top)rdecode_predictions)predsrrvrvrwr\sr)rreterror)Nr r r Tr Tr NNrNrr)NTr NNrrrx)NTr NNrrr~)r rN)N)r)!rL keras.srcrrkeras.src.api_exportrkeras.src.applicationsrkeras.src.modelsr keras.src.opsrkeras.src.utilsrBASE_WEIGHTS_PATHr\r]r_r`rr}rrrrTrRrrPREPROCESS_INPUT_DOCformatPREPROCESS_INPUT_RET_DOC_TFPREPROCESS_INPUT_ERROR_DOC__doc__rvrvrvrws        ) Z S : W ]t