o i e_@sdZddlmmZddlmZddlmZddlm Z ddl m Z ddl m Z ddl mZdd lmZdd lmZd Zd d dddddZe ZdZ            d9ddZed           d:dd Zed!           d:d"d#Zejd d$e_ejd#d$e_d%d&Zd'd(Zd)d*Zd;d,d-Zd.d/Z d0d1Z!ed2dzMobileNet v3 models for Keras.N)backend)models)imagenet_utils)VersionAwareLayers) data_utils) layer_utils) tf_logging) keras_exportzJhttps://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v3/) 765b44a33ad4005b3ac83185abf1d0eb 40af19a13ebea4e2ee0c676887f69a2e) 59e551e166be033d707958cf9e29a6a7 07fb09a5933dd0c8eaafa16978110389) 675e7b876c45c57e9e63e6d90a36599c ec5221f64a2f6d1ef965a614bdae7973) cb65d4e5be93758266aa0a7f2c6708b7 ebdb5cc8e0b497cd13a7c275d475c819) 8768d4c2e7dee89b9d02b2d03d65d862 d3e8ec802a04aa4fc771ee12a9a9b836) 99cd97fb2fcdad2bf028eb838de69e37 cde8136e733e811080d9fcd8a252f7e4)zlarge_224_0.75_floatzlarge_224_1.0_floatz large_minimalistic_224_1.0_floatzsmall_224_0.75_floatzsmall_224_1.0_floatz small_minimalistic_224_1.0_floataInstantiates the {name} architecture. Reference: - [Searching for MobileNetV3]( https://arxiv.org/pdf/1905.02244.pdf) (ICCV 2019) The following table describes the performance of MobileNets v3: ------------------------------------------------------------------------ MACs stands for Multiply Adds |Classification Checkpoint|MACs(M)|Parameters(M)|Top1 Accuracy|Pixel1 CPU(ms)| |---|---|---|---|---| | mobilenet_v3_large_1.0_224 | 217 | 5.4 | 75.6 | 51.2 | | mobilenet_v3_large_0.75_224 | 155 | 4.0 | 73.3 | 39.8 | | mobilenet_v3_large_minimalistic_1.0_224 | 209 | 3.9 | 72.3 | 44.1 | | mobilenet_v3_small_1.0_224 | 66 | 2.9 | 68.1 | 15.8 | | mobilenet_v3_small_0.75_224 | 44 | 2.4 | 65.4 | 12.8 | | mobilenet_v3_small_minimalistic_1.0_224 | 65 | 2.0 | 61.9 | 12.2 | 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 MobileNetV3, by default input preprocessing is included as a part of the model (as a `Rescaling` layer), and thus `tf.keras.applications.mobilenet_v3.preprocess_input` is actually a pass-through function. In this use case, MobileNetV3 models expect their inputs to be float tensors of pixels with values in the [0-255] range. At the same time, preprocessing as a part of the model (i.e. `Rescaling` layer) can be disabled by setting `include_preprocessing` argument to False. With preprocessing disabled MobileNetV3 models expect their inputs to be float tensors of pixels with values in the [-1, 1] range. Args: input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels (224, 224, 3). You can also omit this option if you would like to infer input_shape from an input_tensor. If you choose to include both input_tensor and input_shape then input_shape will be used if they match, if the shapes do not match then we will throw an error. E.g. `(160, 160, 3)` would be one valid value. alpha: controls the width of the network. This is known as the depth multiplier in the MobileNetV3 paper, but the name is kept for consistency with MobileNetV1 in Keras. - 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. minimalistic: In addition to large and small models this module also contains so-called minimalistic models, these models have the same per-layer dimensions characteristic as MobilenetV3 however, they don't utilize any of the advanced blocks (squeeze-and-excite units, hard-swish, and 5x5 convolutions). While these models are less efficient on CPU, they are much more performant on GPU/DSP. include_top: Boolean, whether to include the fully-connected layer at the top of the network. Defaults to `True`. weights: String, 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. pooling: String, 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: Integer, optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. dropout_rate: fraction of the input units to drop on the last layer. 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"`. include_preprocessing: Boolean, whether to include the preprocessing layer (`Rescaling`) at the bottom of the network. Defaults to `True`. Call arguments: inputs: A floating point `numpy.array` or a `tf.Tensor`, 4D with 3 color channels, with values in the range [0, 255] if `include_preprocessing` is True and in the range [-1, 1] otherwise. Returns: A `keras.Model` instance. ?largeFTimagenet皙?softmaxc Cs\|dvstjj|std||dkr#|r#| dkr#td| |dur|durzt|}Wn&tyXz tt|}WntyUtd|dt |wYnw|rt dkrzt |d |d krytd |d t |nt |d |d krtd |d t |ntd|d|dur|durzt|Wntytd|dt |dwt|rt dkrt |d }t |d}d||f}nt |d }t |d }||df}|dur|durd}t dkrd\}}nd\}}||}||}|r&|r&|dks|dkr&td|d|dkrN|s3|dvs;|r?|dkr?td||ksI|dkrNt d|durZtj|d}nt|shtj||d }n|}t dkrsd nd!}|rd}t}d}nd"}t}d#}|}| rtjd$d%d&|}tjd'dd(d)d*d+d,|}tj|d-d.d/d0|}||}|||||}tt ||d1}|dkrt||}tj|d d)d*d2d3|}tj|d-d.d4d0|}||}|r/tjd5d6|}tj|d d)d5d7d3|}||}| d8krt| |}tj| d d)d9d:|}t|}t| |tj| d;d<|}n| d=kr=tjd>d?|}n | d@krJtjdAd?|}|durUt|}n|}tj||dB|d?}|dkrdC||rndDndEt |}|rdF|dG}t!|d8}n dF|dH}t!|d }t"j#|t$|dI|dJ}|%||S|dur|%||S)KN>NrzThe `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=rrzkIf using `weights` as `"imagenet"` with `include_top` as true, `classes` should be 1000. Received classes=zinput_tensor: z7is not type input_tensor. Received type(input_tensor)=channels_firstzWhen backend.image_data_format()=channels_first, input_shape[1] must equal backend.int_shape(input_tensor)[1]. Received input_shape=z", backend.int_shape(input_tensor)=zTinput_shape[1] must equal backend.int_shape(input_tensor)[2]. Received input_shape=zinput_tensor specified: zis not a keras tensorz is type: zwhich is not a valid type)NNr channels_last)rr)rr z9Input size must be at least 32x32; Received `input_shape=`)g?rrz|If imagenet weights are being loaded, alpha can be one of `0.75`, `1.0` for non minimalistic or `1.0` for minimalistic only.z`input_shape` is undefined or non-square, or `rows` is not 224. Weights for input shape (224, 224) will be loaded as the default.)shape)tensorr$g?g?g)scaleoffset)rrsameFConv) kernel_sizestridespaddinguse_biasnameMbP?+?zConv/BatchNormaxisepsilonmomentumr1Conv_1r-r/r0r1zConv_1/BatchNormT)keepdimsConv_2rLogitsr-r/r1 Predictions) activationr1avgavg_poolr1maxmax_pool MobilenetV3z{}{}_224_{}_float _minimalisticweights_mobilenet_v3_z.h5z _no_top_v2.h5r) cache_subdir file_hash)&tfiogfileexists ValueErrorris_keras_tensorrget_source_inputstypeimage_data_format int_shapeloggingwarninglayersInputrelu hard_swish RescalingConv2DBatchNormalization_depthGlobalAveragePooling2DDropoutFlattenrvalidate_activation ActivationGlobalMaxPooling2DrModelformatstrWEIGHTS_HASHESrget_fileBASE_WEIGHT_PATH load_weights) stack_fn last_point_ch input_shapealpha model_type minimalistic include_topweights input_tensorclassespooling dropout_rateclassifier_activationinclude_preprocessingis_input_t_tensorrowscolsrow_axiscol_axis img_input channel_axiskernelr@se_ratiox last_conv_chinputsmodel model_name file_namerK weights_pathrR/var/www/myenv/lib/python3.10/site-packages/keras/src/applications/mobilenet_v3.py MobileNetV3s                               rz#keras.applications.MobileNetV3Smallc .fdd} t| d|d|||||||| | S)Nc s.fdd}t|d|ddd|td}t|d|d dddtd}t|d |d dddtd}t|d |d |d||d}t|d |d |d||d }t|d |d |d||d}t|d|d|d||d }t|d|d|d||d}t|d |d|d||d}t|d |d|d||d}t|d |d|d||d}|S)Nc t|SNr_drprrdepth z1MobileNetV3Small..stack_fn..depthrr*rrrg@gUUUUUU @(r8r'0` _inverted_res_blockrZrrr@rrrrrrms: z"MobileNetV3Small..stack_fnismallr rorprrrsrtrurvrwrxryrzrmrrrMobileNetV3Smalls" !rz#keras.applications.MobileNetV3Largec r)Nc sfdd}t|d|ddddtd}t|d|ddd dtd}t|d|ddddtd }t|d|d |d |td}t|d|d |d|td}t|d|d |d|td }t|d |d dd d|d }t|d|d ddd|d}t|d|d ddd|d}t|d|d ddd|d}t|d |ddd||d}t|d |ddd||d}t|d |d|d ||d}t|d |d|d||d}t|d |d|d||d}|S)Ncrrrrrrrrrz1MobileNetV3Large..stack_fn..depthrr*rrrrrrr'r8Pg@rgffffff@rrpr rrrrrrms6 z"MobileNetV3Large..stack_fnirrrrrrMobileNetV3Larges" rrCcCs t|SrrXReLUrrrrrZ)rrZcCstd|ddS)Ng@g@gUUUUUU?rrrrr hard_sigmoid-srcCst|t|gSr)rXMultiplyrrrrrr[1sr[rcCsB|dur|}t|t||d||}|d|kr||7}|S)Nrg?)rDint)vdivisor min_valuenew_vrrrr_<s  r_cCstjd|dd|}tjt||dd|dd|}tj|dd |}tj|dd|d d|}t|}tj|d d ||g}|S) NTzsqueeze_excite/AvgPool)r;r1rr+zsqueeze_excite/Convr>zsqueeze_excite/RelurCzsqueeze_excite/Conv_1zsqueeze_excite/Mul)rXr`r]r_rrr)rfiltersrprefixrrrr _se_blockFs2 rc Csltdkrdnd}|} d} t||} |r@d|d} tjt| |ddd| d d |}tj|d d | d d|}||}|dkrStjt ||| dd|}tj |||dkr]dndd| dd|}tj|d d | dd|}||}|rt |t| ||| }tj|ddd| dd |}tj|d d | dd|}|dkr| |krtj | dd| |g}|S)Nrrr&zexpanded_conv/expanded_conv_/r+Fexpandr:r2r3zexpand/BatchNormr4rz depthwise/pad)r/r1valid depthwise)r.r/r0r1zdepthwise/BatchNormprojectzproject/BatchNormAddrC) rrTrUrXr]r_r^ ZeroPadding2Dr correct_padDepthwiseConv2Drr) r expansionrr-striderr@block_idrshortcutr infiltersrrrr\s   rz0keras.applications.mobilenet_v3.preprocess_inputcCs|S)aA placeholder method for backward compatibility. The preprocessing logic has been included in the mobilenet_v3 model implementation. Users are no longer required to call this method to normalize the input data. This method does nothing and only kept as a placeholder to align the API surface between old and new version of model. Args: x: A floating point `numpy.array` or a `tf.Tensor`. data_format: Optional data format of the image tensor/array. `None` means the global setting `tf.keras.backend.image_data_format()` is used (unless you changed it, it uses "channels_last"). Defaults to `None`. Returns: Unchanged `numpy.array` or `tf.Tensor`. r)r data_formatrrrpreprocess_inputsrz2keras.applications.mobilenet_v3.decode_predictionsr'cCstj||dS)N)top)rdecode_predictions)predsrrrrrsr) NrrFTrNrNrrT) NrFTrNrNrrT)rNr)r')$__doc__tensorflow.compat.v2compatv2rL keras.srcrrkeras.src.applicationsrkeras.src.layersrkeras.src.utilsrrtensorflow.python.platformrrV tensorflow.python.util.tf_exportr rkrirXBASE_DOCSTRINGrrrrgrZrr[r_rrrrrrrrs        m z @ >  C