o ={cy_@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/)Z 765b44a33ad4005b3ac83185abf1d0ebZ 40af19a13ebea4e2ee0c676887f69a2e)Z 59e551e166be033d707958cf9e29a6a7Z 07fb09a5933dd0c8eaafa16978110389)Z 675e7b876c45c57e9e63e6d90a36599cZ ec5221f64a2f6d1ef965a614bdae7973)Z cb65d4e5be93758266aa0a7f2c6708b7Z ebdb5cc8e0b497cd13a7c275d475c819)Z 8768d4c2e7dee89b9d02b2d03d65d862Z d3e8ec802a04aa4fc771ee12a9a9b836)Z 99cd97fb2fcdad2bf028eb838de69e37Z 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. 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