217 lines
8.7 KiB
Python
217 lines
8.7 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Keras depthwise separable 2D convolution."""
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import tensorflow.compat.v2 as tf
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from keras import activations
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from keras import constraints
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from keras import initializers
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from keras import regularizers
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from keras.layers.convolutional.base_separable_conv import SeparableConv
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from keras.utils import conv_utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export(
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"keras.layers.SeparableConv2D", "keras.layers.SeparableConvolution2D"
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)
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class SeparableConv2D(SeparableConv):
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"""Depthwise separable 2D convolution.
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Separable convolutions consist of first performing
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a depthwise spatial convolution
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(which acts on each input channel separately)
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followed by a pointwise convolution which mixes the resulting
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output channels. The `depth_multiplier` argument controls how many
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output channels are generated per input channel in the depthwise step.
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Intuitively, separable convolutions can be understood as
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a way to factorize a convolution kernel into two smaller kernels,
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or as an extreme version of an Inception block.
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Args:
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filters: Integer, the dimensionality of the output space
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(i.e. the number of output filters in the convolution).
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kernel_size: An integer or tuple/list of 2 integers, specifying the
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height and width of the 2D convolution window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the convolution along the height and width.
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Can be a single integer to specify the same value for
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all spatial dimensions. Current implementation only supports equal
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length strides in the row and column dimensions.
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Specifying any stride value != 1 is incompatible with specifying
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any `dilation_rate` value != 1.
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padding: one of `"valid"` or `"same"` (case-insensitive).
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`"valid"` means no padding. `"same"` results in padding with zeros
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evenly to the left/right or up/down of the input such that output has
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the same height/width dimension as the input.
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape
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`(batch_size, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch_size, channels, height, width)`.
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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "channels_last".
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dilation_rate: An integer or tuple/list of 2 integers, specifying
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the dilation rate to use for dilated convolution.
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depth_multiplier: The number of depthwise convolution output channels
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for each input channel.
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The total number of depthwise convolution output
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channels will be equal to `filters_in * depth_multiplier`.
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activation: Activation function to use.
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If you don't specify anything, no activation is applied
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(see `keras.activations`).
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use_bias: Boolean, whether the layer uses a bias vector.
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depthwise_initializer: An initializer for the depthwise convolution kernel
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(see `keras.initializers`). If None, then the default initializer
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('glorot_uniform') will be used.
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pointwise_initializer: An initializer for the pointwise convolution kernel
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(see `keras.initializers`). If None, then the default initializer
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('glorot_uniform') will be used.
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bias_initializer: An initializer for the bias vector. If None, the default
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initializer ('zeros') will be used (see `keras.initializers`).
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depthwise_regularizer: Regularizer function applied to
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the depthwise kernel matrix (see `keras.regularizers`).
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pointwise_regularizer: Regularizer function applied to
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the pointwise kernel matrix (see `keras.regularizers`).
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bias_regularizer: Regularizer function applied to the bias vector
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(see `keras.regularizers`).
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activity_regularizer: Regularizer function applied to
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the output of the layer (its "activation")
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(see `keras.regularizers`).
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depthwise_constraint: Constraint function applied to
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the depthwise kernel matrix
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(see `keras.constraints`).
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pointwise_constraint: Constraint function applied to
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the pointwise kernel matrix
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(see `keras.constraints`).
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bias_constraint: Constraint function applied to the bias vector
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(see `keras.constraints`).
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Input shape:
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4D tensor with shape:
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`(batch_size, channels, rows, cols)` if data_format='channels_first'
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or 4D tensor with shape:
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`(batch_size, rows, cols, channels)` if data_format='channels_last'.
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Output shape:
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4D tensor with shape:
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`(batch_size, filters, new_rows, new_cols)` if
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data_format='channels_first'
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or 4D tensor with shape:
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`(batch_size, new_rows, new_cols, filters)` if
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data_format='channels_last'. `rows` and `cols` values might have changed
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due to padding.
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Returns:
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A tensor of rank 4 representing
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`activation(separableconv2d(inputs, kernel) + bias)`.
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Raises:
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ValueError: if `padding` is "causal".
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"""
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def __init__(
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self,
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filters,
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kernel_size,
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strides=(1, 1),
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padding="valid",
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data_format=None,
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dilation_rate=(1, 1),
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depth_multiplier=1,
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activation=None,
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use_bias=True,
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depthwise_initializer="glorot_uniform",
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pointwise_initializer="glorot_uniform",
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bias_initializer="zeros",
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depthwise_regularizer=None,
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pointwise_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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depthwise_constraint=None,
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pointwise_constraint=None,
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bias_constraint=None,
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**kwargs
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):
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super().__init__(
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rank=2,
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filters=filters,
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kernel_size=kernel_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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dilation_rate=dilation_rate,
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depth_multiplier=depth_multiplier,
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activation=activations.get(activation),
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use_bias=use_bias,
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depthwise_initializer=initializers.get(depthwise_initializer),
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pointwise_initializer=initializers.get(pointwise_initializer),
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bias_initializer=initializers.get(bias_initializer),
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depthwise_regularizer=regularizers.get(depthwise_regularizer),
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pointwise_regularizer=regularizers.get(pointwise_regularizer),
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bias_regularizer=regularizers.get(bias_regularizer),
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activity_regularizer=regularizers.get(activity_regularizer),
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depthwise_constraint=constraints.get(depthwise_constraint),
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pointwise_constraint=constraints.get(pointwise_constraint),
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bias_constraint=constraints.get(bias_constraint),
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**kwargs
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)
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def call(self, inputs):
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# Apply the actual ops.
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if self.data_format == "channels_last":
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strides = (1,) + self.strides + (1,)
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else:
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strides = (1, 1) + self.strides
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outputs = tf.compat.v1.nn.separable_conv2d(
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inputs,
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self.depthwise_kernel,
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self.pointwise_kernel,
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strides=strides,
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padding=self.padding.upper(),
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rate=self.dilation_rate,
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data_format=conv_utils.convert_data_format(
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self.data_format, ndim=4
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),
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)
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if self.use_bias:
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outputs = tf.nn.bias_add(
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outputs,
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self.bias,
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data_format=conv_utils.convert_data_format(
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self.data_format, ndim=4
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),
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)
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if self.activation is not None:
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return self.activation(outputs)
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return outputs
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# Alias
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SeparableConvolution2D = SeparableConv2D
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