# 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 tf_keras.src import activations from tf_keras.src import constraints from tf_keras.src import initializers from tf_keras.src import regularizers from tf_keras.src.dtensor import utils from tf_keras.src.engine.base_layer import Layer from tf_keras.src.engine.input_spec import InputSpec from tf_keras.src.layers.convolutional.conv1d import Conv1D from tf_keras.src.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 # Call Layer.build() to skip Conv.build() which we override here. Layer.build(self, input_shape) 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