305 lines
12 KiB
Python
305 lines
12 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 1D transposed convolution layer (sometimes called deconvolution)."""
|
|
|
|
|
|
import tensorflow.compat.v2 as tf
|
|
|
|
from keras import activations
|
|
from keras import constraints
|
|
from keras import initializers
|
|
from keras import regularizers
|
|
from keras.dtensor import utils
|
|
from keras.engine.input_spec import InputSpec
|
|
from keras.layers.convolutional.conv1d import Conv1D
|
|
from keras.utils import conv_utils
|
|
|
|
# isort: off
|
|
from tensorflow.python.util.tf_export import keras_export
|
|
|
|
|
|
@keras_export(
|
|
"keras.layers.Conv1DTranspose", "keras.layers.Convolution1DTranspose"
|
|
)
|
|
class Conv1DTranspose(Conv1D):
|
|
"""Transposed convolution layer (sometimes called Deconvolution).
|
|
|
|
The need for transposed convolutions generally arises
|
|
from the desire to use a transformation going in the opposite direction
|
|
of a normal convolution, i.e., from something that has the shape of the
|
|
output of some convolution to something that has the shape of its input
|
|
while maintaining a connectivity pattern that is compatible with
|
|
said convolution.
|
|
|
|
When using this layer as the first layer in a model,
|
|
provide the keyword argument `input_shape`
|
|
(tuple of integers or `None`, does not include the sample axis),
|
|
e.g. `input_shape=(128, 3)` for data with 128 time steps and 3 channels.
|
|
|
|
Args:
|
|
filters: Integer, the dimensionality of the output space
|
|
(i.e. the number of output filters in the convolution).
|
|
kernel_size: An integer length of the 1D convolution window.
|
|
strides: An integer specifying the stride of the convolution along the
|
|
time dimension. Specifying a stride value != 1 is incompatible with
|
|
specifying a `dilation_rate` value != 1. Defaults to 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.
|
|
output_padding: An integer specifying the amount of padding along
|
|
the time dimension of the output tensor.
|
|
The amount of output padding must be lower than the stride.
|
|
If set to `None` (default), the output shape is inferred.
|
|
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, length, channels)` while `channels_first` corresponds to
|
|
inputs with shape `(batch_size, channels, length)`.
|
|
dilation_rate: an integer, specifying
|
|
the dilation rate to use for dilated convolution.
|
|
Currently, specifying a `dilation_rate` value != 1 is
|
|
incompatible with specifying a stride value != 1.
|
|
Also dilation rate larger than 1 is not currently supported.
|
|
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.
|
|
kernel_initializer: Initializer for the `kernel` weights matrix
|
|
(see `keras.initializers`). Defaults to 'glorot_uniform'.
|
|
bias_initializer: Initializer for the bias vector
|
|
(see `keras.initializers`). Defaults to 'zeros'.
|
|
kernel_regularizer: Regularizer function applied to
|
|
the `kernel` weights 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`).
|
|
kernel_constraint: Constraint function applied to the 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, steps, channels)`
|
|
|
|
Output shape:
|
|
3D tensor with shape:
|
|
`(batch_size, new_steps, filters)`
|
|
If `output_padding` is specified:
|
|
```
|
|
new_timesteps = ((timesteps - 1) * strides + kernel_size -
|
|
2 * padding + output_padding)
|
|
```
|
|
|
|
Returns:
|
|
A tensor of rank 3 representing
|
|
`activation(conv1dtranspose(inputs, kernel) + bias)`.
|
|
|
|
Raises:
|
|
ValueError: if `padding` is "causal".
|
|
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
|
|
|
|
References:
|
|
- [A guide to convolution arithmetic for deep learning](
|
|
https://arxiv.org/abs/1603.07285v1)
|
|
- [Deconvolutional Networks](
|
|
https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
|
|
"""
|
|
|
|
@utils.allow_initializer_layout
|
|
def __init__(
|
|
self,
|
|
filters,
|
|
kernel_size,
|
|
strides=1,
|
|
padding="valid",
|
|
output_padding=None,
|
|
data_format=None,
|
|
dilation_rate=1,
|
|
activation=None,
|
|
use_bias=True,
|
|
kernel_initializer="glorot_uniform",
|
|
bias_initializer="zeros",
|
|
kernel_regularizer=None,
|
|
bias_regularizer=None,
|
|
activity_regularizer=None,
|
|
kernel_constraint=None,
|
|
bias_constraint=None,
|
|
**kwargs,
|
|
):
|
|
super().__init__(
|
|
filters=filters,
|
|
kernel_size=kernel_size,
|
|
strides=strides,
|
|
padding=padding,
|
|
data_format=data_format,
|
|
dilation_rate=dilation_rate,
|
|
activation=activations.get(activation),
|
|
use_bias=use_bias,
|
|
kernel_initializer=initializers.get(kernel_initializer),
|
|
bias_initializer=initializers.get(bias_initializer),
|
|
kernel_regularizer=regularizers.get(kernel_regularizer),
|
|
bias_regularizer=regularizers.get(bias_regularizer),
|
|
activity_regularizer=regularizers.get(activity_regularizer),
|
|
kernel_constraint=constraints.get(kernel_constraint),
|
|
bias_constraint=constraints.get(bias_constraint),
|
|
**kwargs,
|
|
)
|
|
|
|
self.output_padding = output_padding
|
|
if self.output_padding is not None:
|
|
self.output_padding = conv_utils.normalize_tuple(
|
|
self.output_padding, 1, "output_padding", allow_zero=True
|
|
)
|
|
for stride, out_pad in zip(self.strides, self.output_padding):
|
|
if out_pad >= stride:
|
|
raise ValueError(
|
|
"Strides must be greater than output padding. "
|
|
f"Received strides={self.strides}, "
|
|
f"output_padding={self.output_padding}."
|
|
)
|
|
|
|
def build(self, input_shape):
|
|
input_shape = tf.TensorShape(input_shape)
|
|
if len(input_shape) != 3:
|
|
raise ValueError(
|
|
"Inputs should have rank 3. "
|
|
f"Received input_shape={input_shape}."
|
|
)
|
|
channel_axis = self._get_channel_axis()
|
|
if input_shape.dims[channel_axis].value is None:
|
|
raise ValueError(
|
|
"The channel dimension of the inputs "
|
|
"to `Conv1DTranspose` should be defined. "
|
|
f"The input_shape received is {input_shape}, "
|
|
f"where axis {channel_axis} (0-based) "
|
|
"is the channel dimension, which found to be `None`."
|
|
)
|
|
input_dim = int(input_shape[channel_axis])
|
|
self.input_spec = InputSpec(ndim=3, axes={channel_axis: input_dim})
|
|
kernel_shape = self.kernel_size + (self.filters, input_dim)
|
|
|
|
self.kernel = self.add_weight(
|
|
name="kernel",
|
|
shape=kernel_shape,
|
|
initializer=self.kernel_initializer,
|
|
regularizer=self.kernel_regularizer,
|
|
constraint=self.kernel_constraint,
|
|
trainable=True,
|
|
dtype=self.dtype,
|
|
)
|
|
if self.use_bias:
|
|
self.bias = self.add_weight(
|
|
name="bias",
|
|
shape=(self.filters,),
|
|
initializer=self.bias_initializer,
|
|
regularizer=self.bias_regularizer,
|
|
constraint=self.bias_constraint,
|
|
trainable=True,
|
|
dtype=self.dtype,
|
|
)
|
|
else:
|
|
self.bias = None
|
|
self.built = True
|
|
|
|
def call(self, inputs):
|
|
inputs_shape = tf.shape(inputs)
|
|
batch_size = inputs_shape[0]
|
|
if self.data_format == "channels_first":
|
|
t_axis = 2
|
|
else:
|
|
t_axis = 1
|
|
|
|
length = inputs_shape[t_axis]
|
|
if self.output_padding is None:
|
|
output_padding = None
|
|
else:
|
|
output_padding = self.output_padding[0]
|
|
|
|
# Infer the dynamic output shape:
|
|
out_length = conv_utils.deconv_output_length(
|
|
length,
|
|
self.kernel_size[0],
|
|
padding=self.padding,
|
|
output_padding=output_padding,
|
|
stride=self.strides[0],
|
|
dilation=self.dilation_rate[0],
|
|
)
|
|
if self.data_format == "channels_first":
|
|
output_shape = (batch_size, self.filters, out_length)
|
|
else:
|
|
output_shape = (batch_size, out_length, self.filters)
|
|
data_format = conv_utils.convert_data_format(self.data_format, ndim=3)
|
|
|
|
output_shape_tensor = tf.stack(output_shape)
|
|
outputs = tf.nn.conv1d_transpose(
|
|
inputs,
|
|
self.kernel,
|
|
output_shape_tensor,
|
|
strides=self.strides,
|
|
padding=self.padding.upper(),
|
|
data_format=data_format,
|
|
dilations=self.dilation_rate,
|
|
)
|
|
|
|
if not tf.executing_eagerly() and inputs.shape.rank:
|
|
# Infer the static output shape:
|
|
out_shape = self.compute_output_shape(inputs.shape)
|
|
outputs.set_shape(out_shape)
|
|
|
|
if self.use_bias:
|
|
outputs = tf.nn.bias_add(
|
|
outputs, self.bias, data_format=data_format
|
|
)
|
|
|
|
if self.activation is not None:
|
|
return self.activation(outputs)
|
|
return outputs
|
|
|
|
def compute_output_shape(self, input_shape):
|
|
input_shape = tf.TensorShape(input_shape).as_list()
|
|
output_shape = list(input_shape)
|
|
if self.data_format == "channels_first":
|
|
c_axis, t_axis = 1, 2
|
|
else:
|
|
c_axis, t_axis = 2, 1
|
|
|
|
if self.output_padding is None:
|
|
output_padding = None
|
|
else:
|
|
output_padding = self.output_padding[0]
|
|
output_shape[c_axis] = self.filters
|
|
output_shape[t_axis] = conv_utils.deconv_output_length(
|
|
output_shape[t_axis],
|
|
self.kernel_size[0],
|
|
padding=self.padding,
|
|
output_padding=output_padding,
|
|
stride=self.strides[0],
|
|
dilation=self.dilation_rate[0],
|
|
)
|
|
return tf.TensorShape(output_shape)
|
|
|
|
def get_config(self):
|
|
config = super().get_config()
|
|
config["output_padding"] = self.output_padding
|
|
return config
|
|
|
|
|
|
# Alias
|
|
|
|
Convolution1DTranspose = Conv1DTranspose
|