6bi7"pSrSSKJr SSKJr SSKJr SSKJr SSKJ r \ "S5"SS \55r g ) zKeras depthwise 2D convolution.)backend) DepthwiseConv) conv_utils)tf_utils) keras_exportzkeras.layers.DepthwiseConv2Dcv^\rSrSrSrSU4SjjrSr\RS5r Sr U=r $)DepthwiseConv2DaDepthwise 2D convolution. Depthwise convolution is a type of convolution in which each input channel is convolved with a different kernel (called a depthwise kernel). You can understand depthwise convolution as the first step in a depthwise separable convolution. It is implemented via the following steps: - Split the input into individual channels. - Convolve each channel with an individual depthwise kernel with `depth_multiplier` output channels. - Concatenate the convolved outputs along the channels axis. Unlike a regular 2D convolution, depthwise convolution does not mix information across different input channels. The `depth_multiplier` argument determines how many filter are applied to one input channel. As such, it controls the amount of output channels that are generated per input channel in the depthwise step. Args: kernel_size: An integer or tuple/list of 2 integers, specifying the height and width 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 height and width. Can be a single integer to specify the same value for all spatial dimensions. Current implementation only supports equal length strides in row and column dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value !=1. padding: one of `'valid'` or `'same'` (case-insensitive). `"valid"` means no padding. `"same"` results in padding with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. 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`. data_format: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch_size, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch_size, channels, height, width)`. When unspecified, uses `image_data_format` value found in your TF-Keras config file at `~/.keras/keras.json` (if exists) else 'channels_last'. Defaults to 'channels_last'. dilation_rate: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilation_rate` value != 1 is incompatible with specifying any `strides` value != 1. activation: Activation function to use. If you don't specify anything, no activation is applied (see `keras.activations`). use_bias: Boolean, whether the layer uses a bias vector. depthwise_initializer: Initializer for the depthwise kernel matrix (see `keras.initializers`). If None, the default initializer ('glorot_uniform') will be used. bias_initializer: Initializer for the bias vector (see `keras.initializers`). If None, the default initializer ('zeros') will be used. depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix (see `keras.regularizers`). bias_regularizer: Regularizer function applied to the bias vector (see `keras.regularizers`). activity_regularizer: Regularizer function applied to the output of the layer (its 'activation') (see `keras.regularizers`). depthwise_constraint: Constraint function applied to the depthwise kernel matrix (see `keras.constraints`). bias_constraint: Constraint function applied to the bias vector (see `keras.constraints`). Input shape: 4D tensor with shape: `[batch_size, channels, rows, cols]` if data_format='channels_first' or 4D tensor with shape: `[batch_size, rows, cols, channels]` if data_format='channels_last'. Output shape: 4D tensor with shape: `[batch_size, channels * depth_multiplier, new_rows, new_cols]` if `data_format='channels_first'` or 4D tensor with shape: `[batch_size, new_rows, new_cols, channels * depth_multiplier]` if `data_format='channels_last'`. `rows` and `cols` values might have changed due to padding. Returns: A tensor of rank 4 representing `activation(depthwiseconv2d(inputs, kernel) + bias)`. Raises: ValueError: if `padding` is "causal". ValueError: when both `strides` > 1 and `dilation_rate` > 1. c H>[TU]"SUUUUUUUUU U U U U UUS.UD6 g)N) kernel_sizestridespaddingdepth_multiplier data_format dilation_rate activationuse_biasdepthwise_initializerbias_initializerdepthwise_regularizerbias_regularizeractivity_regularizerdepthwise_constraintbias_constraint))super__init__)selfr r rrrrrrrrrrrrrkwargs __class__s l/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/layers/convolutional/depthwise_conv2d.pyrDepthwiseConv2D.__init__zsQ&   #-#'!"7-"7-!5!5+! "# c L[R"UURURURUR UR S9nUR(a)[R"X RUR S9nURbURU5$U$)N)r rrr)r) rdepthwise_conv2ddepthwise_kernelr rrrrbias_addbiasr)rinputsoutputss r!callDepthwiseConv2D.calls**   ! !LLLL,,((   ==&&0@0@G ?? &??7+ +r#cBURS:XaUSnUSnUSUR-nO,URS:XaUSnUSnUSUR-n[R"WURSUR UR SURS5n[R"WURSUR UR SURS5nURS:XaUSWX#4$URS:XaUSX#W4$g)Nchannels_firstr channels_lastr)rrrconv_output_lengthr rr r)r input_shaperowscols out_filterss r!compute_output_shape$DepthwiseConv2D.compute_output_shapes5   / /q>Dq>D%a.4+@+@@K    0q>Dq>D%a.4+@+@@K,,    Q  LL LLO   q !  ,,    Q  LL LLO   q !     / /NK< <    0ND < <1r#)r0r0validr0Nr:NTglorot_uniformzerosNNNNN) __name__ __module__ __qualname____firstlineno____doc__rr+rshape_type_conversionr7__static_attributes__ __classcell__)r s@r!r r s[[@. "!!!% N(##=$=r#r N) rB tf_keras.srcr5tf_keras.src.layers.convolutional.base_depthwise_convrtf_keras.src.utilsrr tensorflow.python.util.tf_exportrr r9r#r!rJsA&!O)':,-u=mu=.u=r#