218 lines
8.7 KiB
Python
218 lines
8.7 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""Keras depthwise 1D convolution."""
|
|
|
|
|
|
import tensorflow.compat.v2 as tf
|
|
|
|
from keras.layers.convolutional.base_depthwise_conv import DepthwiseConv
|
|
from keras.utils import conv_utils
|
|
from keras.utils import tf_utils
|
|
|
|
# isort: off
|
|
from tensorflow.python.util.tf_export import keras_export
|
|
|
|
|
|
@keras_export("keras.layers.DepthwiseConv1D")
|
|
class DepthwiseConv1D(DepthwiseConv):
|
|
"""Depthwise 1D 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 1D 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, specifying the height and width of the 1D
|
|
convolution window. Can be a single integer to specify the same value
|
|
for all spatial dimensions.
|
|
strides: An integer, 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. 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)`. It defaults to the
|
|
`image_data_format` value found in your Keras config file at
|
|
`~/.keras/keras.json`. If you never set it, then it will be
|
|
'channels_last'.
|
|
dilation_rate: A single integer, specifying the dilation rate to use for
|
|
dilated convolution. Currently, specifying any `dilation_rate`
|
|
value != 1 is incompatible with specifying any stride 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:
|
|
3D tensor with shape: `[batch_size, channels, input_dim]` if
|
|
data_format='channels_first'
|
|
or 3D tensor with shape: `[batch_size, input_dim, channels]` if
|
|
data_format='channels_last'.
|
|
|
|
Output shape:
|
|
3D tensor with shape:
|
|
`[batch_size, channels * depth_multiplier, new_dims]`
|
|
if `data_format='channels_first'`
|
|
or 3D tensor with shape: `[batch_size,
|
|
new_dims, channels * depth_multiplier]` if
|
|
`data_format='channels_last'`. `new_dims` values might have
|
|
changed due to padding.
|
|
|
|
Returns:
|
|
A tensor of rank 3 representing
|
|
`activation(depthwiseconv1d(inputs, kernel) + bias)`.
|
|
|
|
Raises:
|
|
ValueError: if `padding` is "causal".
|
|
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
kernel_size,
|
|
strides=1,
|
|
padding="valid",
|
|
depth_multiplier=1,
|
|
data_format=None,
|
|
dilation_rate=1,
|
|
activation=None,
|
|
use_bias=True,
|
|
depthwise_initializer="glorot_uniform",
|
|
bias_initializer="zeros",
|
|
depthwise_regularizer=None,
|
|
bias_regularizer=None,
|
|
activity_regularizer=None,
|
|
depthwise_constraint=None,
|
|
bias_constraint=None,
|
|
**kwargs
|
|
):
|
|
super().__init__(
|
|
1,
|
|
kernel_size=kernel_size,
|
|
strides=strides,
|
|
padding=padding,
|
|
depth_multiplier=depth_multiplier,
|
|
data_format=data_format,
|
|
dilation_rate=dilation_rate,
|
|
activation=activation,
|
|
use_bias=use_bias,
|
|
depthwise_initializer=depthwise_initializer,
|
|
bias_initializer=bias_initializer,
|
|
depthwise_regularizer=depthwise_regularizer,
|
|
bias_regularizer=bias_regularizer,
|
|
activity_regularizer=activity_regularizer,
|
|
depthwise_constraint=depthwise_constraint,
|
|
bias_constraint=bias_constraint,
|
|
**kwargs
|
|
)
|
|
|
|
def call(self, inputs):
|
|
if self.data_format == "channels_last":
|
|
strides = (1,) + self.strides * 2 + (1,)
|
|
spatial_start_dim = 1
|
|
else:
|
|
strides = (1, 1) + self.strides * 2
|
|
spatial_start_dim = 2
|
|
inputs = tf.expand_dims(inputs, spatial_start_dim)
|
|
depthwise_kernel = tf.expand_dims(self.depthwise_kernel, axis=0)
|
|
dilation_rate = (1,) + self.dilation_rate
|
|
|
|
outputs = tf.nn.depthwise_conv2d(
|
|
inputs,
|
|
depthwise_kernel,
|
|
strides=strides,
|
|
padding=self.padding.upper(),
|
|
dilations=dilation_rate,
|
|
data_format=conv_utils.convert_data_format(
|
|
self.data_format, ndim=4
|
|
),
|
|
)
|
|
|
|
if self.use_bias:
|
|
outputs = tf.nn.bias_add(
|
|
outputs,
|
|
self.bias,
|
|
data_format=conv_utils.convert_data_format(
|
|
self.data_format, ndim=4
|
|
),
|
|
)
|
|
|
|
outputs = tf.squeeze(outputs, [spatial_start_dim])
|
|
|
|
if self.activation is not None:
|
|
return self.activation(outputs)
|
|
|
|
return outputs
|
|
|
|
@tf_utils.shape_type_conversion
|
|
def compute_output_shape(self, input_shape):
|
|
if self.data_format == "channels_first":
|
|
input_dim = input_shape[2]
|
|
out_filters = input_shape[1] * self.depth_multiplier
|
|
elif self.data_format == "channels_last":
|
|
input_dim = input_shape[1]
|
|
out_filters = input_shape[2] * self.depth_multiplier
|
|
|
|
input_dim = conv_utils.conv_output_length(
|
|
input_dim,
|
|
self.kernel_size[0],
|
|
self.padding,
|
|
self.strides[0],
|
|
self.dilation_rate[0],
|
|
)
|
|
if self.data_format == "channels_first":
|
|
return (input_shape[0], out_filters, input_dim)
|
|
elif self.data_format == "channels_last":
|
|
return (input_shape[0], input_dim, out_filters)
|