o i e@@sdZddlmZddlmZddlmZddlmZddlmZddlmZddl m Z dd l m Z dd l mZdd l mZdd lmZed GdddeZdS)z%Locally-connected layer for 2D input.) activations)backend) constraints) initializers) regularizers)Layer) InputSpec)locally_connected_utils) conv_utils)tf_utils) keras_exportzkeras.layers.LocallyConnected2DcsxeZdZdZ             dfd d Zed d Zejd dZ ejddZ ddZ fddZ Z S)LocallyConnected2DaLocally-connected layer for 2D inputs. The `LocallyConnected2D` layer works similarly to the `Conv2D` layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. Note: layer attributes cannot be modified after the layer has been called once (except the `trainable` attribute). Examples: ```python # apply a 3x3 unshared weights convolution with 64 output filters on a 32x32 image # with `data_format="channels_last"`: model = Sequential() model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3))) # now model.output_shape == (None, 30, 30, 64) # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64 parameters # add a 3x3 unshared weights convolution on top, with 32 output filters: model.add(LocallyConnected2D(32, (3, 3))) # now model.output_shape == (None, 28, 28, 32) ``` Args: filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: 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. padding: Currently only support `"valid"` (case-insensitive). `"same"` will be supported in future. `"valid"` means no padding. 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. When unspecified, uses `image_data_format` value found in your Keras config file at `~/.keras/keras.json` (if exists) else 'channels_last'. Defaults to 'channels_last'. activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). kernel_constraint: Constraint function applied to the kernel matrix. bias_constraint: Constraint function applied to the bias vector. implementation: implementation mode, either `1`, `2`, or `3`. `1` loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops. `2` stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops. `3` stores layer weights in a sparse tensor and implements the forward pass as a single sparse matrix-multiply. How to choose: `1`: large, dense models, `2`: small models, `3`: large, sparse models, where "large" stands for large input/output activations (i.e. many `filters`, `input_filters`, large `np.prod(input_size)`, `np.prod(output_size)`), and "sparse" stands for few connections between inputs and outputs, i.e. small ratio `filters * input_filters * np.prod(kernel_size) / (np.prod(input_size) * np.prod(strides))`, where inputs to and outputs of the layer are assumed to have shapes `input_size + (input_filters,)`, `output_size + (filters,)` respectively. It is recommended to benchmark each in the setting of interest to pick the most efficient one (in terms of speed and memory usage). Correct choice of implementation can lead to dramatic speed improvements (e.g. 50X), potentially at the expense of RAM. Also, only `padding="valid"` is supported by `implementation=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 padding. rvalidNTglorot_uniformzerosrc stjd i|||_t|dd|_tj|dddd|_t||_|jdkr3|dkr3t d|t ||_ t ||_||_t ||_t | |_t | |_t | |_t | |_t | |_t ||_||_td d |_dS) N kernel_sizestridesT) allow_zerorrz_Invalid border mode for LocallyConnected2D (only "valid" is supported if implementation is 1): )ndim)super__init__filtersr normalize_tuplerrnormalize_paddingpadding ValueErrornormalize_data_format data_formatrget activationuse_biasrkernel_initializerbias_initializerrkernel_regularizerbias_regularizeractivity_regularizerrkernel_constraintbias_constraintimplementationr input_spec)selfrrrrr"r$r%r&r'r(r)r*r+r,r-kwargs __class__re/var/www/myenv/lib/python3.10/site-packages/keras/src/layers/locally_connected/locally_connected2d.pyrs6          zLocallyConnected2D.__init__cCsdS)NFr)r/rrr3!_use_input_spec_as_call_signaturesz4LocallyConnected2D._use_input_spec_as_call_signaturec Cs|jdkr|dd\}}|d}n |dd\}}|d}|dus&|dur.tdt|t||jd|j|jd}t||jd|j|jd}||_||_ |jdks\|j dkrhtd|j d |d |j dkr|||jd|jd||j f|_ |j|j |jd |j|jd |_n|j dkr|jd kr||||j |j|j f|_ n ||||j|j |j f|_ |j|j |jd |j|jd |_tj||f|j|j|j|jd|_nF|j dkr|j|j |j |||f|_ ttj||f|j|j|j||j |jd|_|jt|jf|jd |j|jd |_ntd|j |jr1|j|||j f|jd|j|jd |_nd|_|jd krDtdd|id|_ n tdd|id|_ d|_!dS)N channels_lastrrzThe spatial dimensions of the inputs to a LocallyConnected2D layer should be fully-defined, but layer received the inputs shape rzCOne of the dimensions in the output is <= 0 due to downsampling in z;. 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Received input shape zO which would produce output shape with a zero or negative value in a dimension.kernel)shape initializername regularizer constraintchannels_first) input_shape kernel_shaperrr")r?r@rr filters_in filters_outr"%Unrecognized implementation mode: %d.biasr)raxesT)"r"r strr conv_output_lengthrrr output_row output_colr;r-rr@ add_weightr&r(r+r8r get_locallyconnected_mask kernel_masksortedconv_kernel_idxs kernel_idxslenr%r'r)r,rDrr.built)r/r? input_row input_col input_filterrHrIrrr3builds                 zLocallyConnected2D.buildcCs|jdkr|d}|d}n |jdkr|d}|d}t||jd|j|jd}t||jd|j|jd}|jdkrG|d|j||fS|jdkrU|d|||jfSdS)Nr>rr7r5rr)r"r rGrrrr)r/r?rowscolsrrr3compute_output_shape9s"     z'LocallyConnected2D.compute_output_shapec Cs|jdkrt||j|j|j|j|jf|j}n1|jdkr,t ||j|j | |j }n|jdkrBt ||j|j|j| |j }ntd|j|jrVtj||j|jd}||}|S)Nrrr7rC)r")r-r local_convr8rrrHrIr"r local_conv_matmulrLrXr9local_conv_sparse_matmulrOr@r r%bias_addrDr$)r/inputsoutputrrr3callNsB         zLocallyConnected2D.callcs|j|j|j|j|jt|j|jt |j t |j t |j t |jt |jt|jt|j|jd}t}tt|t|S)N)rrrrr"r$r%r&r'r(r)r*r+r,r-)rrrrr"r serializer$r%rr&r'rr(r)r*rr+r,r-r get_configdictlistitems)r/config base_configr1rr3raws0      zLocallyConnected2D.get_config) rrNNTrrNNNNNr)__name__ __module__ __qualname____doc__rpropertyr4r shape_type_conversionrUrXr_ra __classcell__rrr1r3r !s2b.   )r N)rj keras.srcrrrrrkeras.src.engine.base_layerrkeras.src.engine.input_specr"keras.src.layers.locally_connectedr keras.src.utilsr r tensorflow.python.util.tf_exportr r rrrr3s