218 lines
8.7 KiB
Python
218 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 1D convolution."""
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import tensorflow.compat.v2 as tf
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from keras.layers.convolutional.base_depthwise_conv import DepthwiseConv
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from keras.utils import conv_utils
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from keras.utils import tf_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("keras.layers.DepthwiseConv1D")
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class DepthwiseConv1D(DepthwiseConv):
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"""Depthwise 1D convolution.
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Depthwise convolution is a type of convolution in which each input channel
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is convolved with a different kernel (called a depthwise kernel). You can
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understand depthwise convolution as the first step in a depthwise separable
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convolution.
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It is implemented via the following steps:
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- Split the input into individual channels.
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- Convolve each channel with an individual depthwise kernel with
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`depth_multiplier` output channels.
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- Concatenate the convolved outputs along the channels axis.
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Unlike a regular 1D convolution, depthwise convolution does not mix
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information across different input channels.
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The `depth_multiplier` argument determines how many filter are applied to
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one input channel. As such, it controls the amount of output channels that
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are generated per input channel in the depthwise step.
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Args:
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kernel_size: An integer, specifying the height and width of the 1D
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convolution window. Can be a single integer to specify the same value
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for all spatial dimensions.
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strides: An integer, specifying the strides of the convolution along the
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height and width. Can be a single integer to specify the same value for
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all spatial dimensions. Specifying any stride value != 1 is incompatible
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with specifying any `dilation_rate` value != 1.
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padding: one of `'valid'` or `'same'` (case-insensitive). `"valid"` means
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no padding. `"same"` results in padding with zeros evenly to the
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left/right or up/down of the input such that output has the same
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height/width dimension as the input.
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depth_multiplier: The number of depthwise convolution output channels for
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each input channel. The total number of depthwise convolution output
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channels will be equal to `filters_in * depth_multiplier`.
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data_format: A string, one of `channels_last` (default) or
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`channels_first`. The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape `(batch_size, height,
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width, channels)` while `channels_first` corresponds to inputs with
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shape `(batch_size, channels, height, width)`. It defaults to the
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`image_data_format` value found in your Keras config file at
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`~/.keras/keras.json`. If you never set it, then it will be
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'channels_last'.
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dilation_rate: A single integer, specifying the dilation rate to use for
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dilated convolution. Currently, specifying any `dilation_rate`
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value != 1 is incompatible with specifying any stride value != 1.
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activation: Activation function to use. If you don't specify anything, no
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activation is applied (see `keras.activations`).
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use_bias: Boolean, whether the layer uses a bias vector.
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depthwise_initializer: Initializer for the depthwise kernel matrix (see
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`keras.initializers`). If None, the default initializer
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('glorot_uniform') will be used.
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bias_initializer: Initializer for the bias vector (see
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`keras.initializers`). If None, the default initializer ('zeros') will
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be used.
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depthwise_regularizer: Regularizer function applied to the depthwise
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kernel matrix (see `keras.regularizers`).
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bias_regularizer: Regularizer function applied to the bias vector (see
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`keras.regularizers`).
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activity_regularizer: Regularizer function applied to the output of the
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layer (its 'activation') (see `keras.regularizers`).
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depthwise_constraint: Constraint function applied to the depthwise kernel
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matrix (see `keras.constraints`).
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bias_constraint: Constraint function applied to the bias vector (see
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`keras.constraints`).
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Input shape:
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3D tensor with shape: `[batch_size, channels, input_dim]` if
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data_format='channels_first'
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or 3D tensor with shape: `[batch_size, input_dim, channels]` if
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data_format='channels_last'.
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Output shape:
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3D tensor with shape:
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`[batch_size, channels * depth_multiplier, new_dims]`
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if `data_format='channels_first'`
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or 3D tensor with shape: `[batch_size,
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new_dims, channels * depth_multiplier]` if
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`data_format='channels_last'`. `new_dims` values might have
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changed due to padding.
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Returns:
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A tensor of rank 3 representing
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`activation(depthwiseconv1d(inputs, kernel) + bias)`.
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Raises:
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ValueError: if `padding` is "causal".
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ValueError: when both `strides` > 1 and `dilation_rate` > 1.
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"""
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def __init__(
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self,
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kernel_size,
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strides=1,
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padding="valid",
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depth_multiplier=1,
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data_format=None,
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dilation_rate=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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bias_initializer="zeros",
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depthwise_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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bias_constraint=None,
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**kwargs
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):
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super().__init__(
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1,
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kernel_size=kernel_size,
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strides=strides,
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padding=padding,
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depth_multiplier=depth_multiplier,
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data_format=data_format,
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dilation_rate=dilation_rate,
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activation=activation,
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use_bias=use_bias,
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depthwise_initializer=depthwise_initializer,
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bias_initializer=bias_initializer,
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depthwise_regularizer=depthwise_regularizer,
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bias_regularizer=bias_regularizer,
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activity_regularizer=activity_regularizer,
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depthwise_constraint=depthwise_constraint,
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bias_constraint=bias_constraint,
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**kwargs
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)
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def call(self, inputs):
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if self.data_format == "channels_last":
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strides = (1,) + self.strides * 2 + (1,)
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spatial_start_dim = 1
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else:
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strides = (1, 1) + self.strides * 2
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spatial_start_dim = 2
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inputs = tf.expand_dims(inputs, spatial_start_dim)
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depthwise_kernel = tf.expand_dims(self.depthwise_kernel, axis=0)
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dilation_rate = (1,) + self.dilation_rate
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outputs = tf.nn.depthwise_conv2d(
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inputs,
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depthwise_kernel,
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strides=strides,
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padding=self.padding.upper(),
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dilations=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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outputs = tf.squeeze(outputs, [spatial_start_dim])
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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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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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if self.data_format == "channels_first":
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input_dim = input_shape[2]
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out_filters = input_shape[1] * self.depth_multiplier
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elif self.data_format == "channels_last":
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input_dim = input_shape[1]
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out_filters = input_shape[2] * self.depth_multiplier
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input_dim = conv_utils.conv_output_length(
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input_dim,
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self.kernel_size[0],
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self.padding,
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self.strides[0],
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self.dilation_rate[0],
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)
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if self.data_format == "channels_first":
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return (input_shape[0], out_filters, input_dim)
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elif self.data_format == "channels_last":
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return (input_shape[0], input_dim, out_filters)
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