# 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 2D transposed convolution layer (sometimes called deconvolution).""" import tensorflow.compat.v2 as tf from keras import activations from keras import backend 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.conv2d import Conv2D from keras.utils import conv_utils # isort: off from tensorflow.python.util.tf_export import keras_export @keras_export( "keras.layers.Conv2DTranspose", "keras.layers.Convolution2DTranspose" ) class Conv2DTranspose(Conv2D): """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, 128, 3)` for 128x128 RGB pictures in `data_format="channels_last"`. Args: filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). kernel_size: An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, 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. output_padding: An integer or tuple/list of 2 integers, specifying the amount of padding along the height and width of the output tensor. Can be a single integer to specify the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. 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, 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: an integer, specifying the dilation rate for all spatial dimensions for dilated convolution. Specifying different dilation rates for different dimensions is not supported. 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. 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: 4D tensor with shape: `(batch_size, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(batch_size, rows, cols, channels)` if data_format='channels_last'. Output shape: 4D tensor with shape: `(batch_size, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(batch_size, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to padding. If `output_padding` is specified: ``` new_rows = ((rows - 1) * strides[0] + kernel_size[0] - 2 * padding[0] + output_padding[0]) new_cols = ((cols - 1) * strides[1] + kernel_size[1] - 2 * padding[1] + output_padding[1]) ``` Returns: A tensor of rank 4 representing `activation(conv2dtranspose(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, 1), padding="valid", output_padding=None, data_format=None, dilation_rate=(1, 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, 2, "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) != 4: raise ValueError( "Inputs should have rank 4. " 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 `Conv2DTranspose` 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=4, 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": h_axis, w_axis = 2, 3 else: h_axis, w_axis = 1, 2 # Use the constant height and weight when possible. # TODO(scottzhu): Extract this into a utility function that can be # applied to all convolutional layers, which currently lost the static # shape information due to tf.shape(). height, width = None, None if inputs.shape.rank is not None: dims = inputs.shape.as_list() height = dims[h_axis] width = dims[w_axis] height = height if height is not None else inputs_shape[h_axis] width = width if width is not None else inputs_shape[w_axis] kernel_h, kernel_w = self.kernel_size stride_h, stride_w = self.strides if self.output_padding is None: out_pad_h = out_pad_w = None else: out_pad_h, out_pad_w = self.output_padding # Infer the dynamic output shape: out_height = conv_utils.deconv_output_length( height, kernel_h, padding=self.padding, output_padding=out_pad_h, stride=stride_h, dilation=self.dilation_rate[0], ) out_width = conv_utils.deconv_output_length( width, kernel_w, padding=self.padding, output_padding=out_pad_w, stride=stride_w, dilation=self.dilation_rate[1], ) if self.data_format == "channels_first": output_shape = (batch_size, self.filters, out_height, out_width) else: output_shape = (batch_size, out_height, out_width, self.filters) output_shape_tensor = tf.stack(output_shape) outputs = backend.conv2d_transpose( inputs, self.kernel, output_shape_tensor, strides=self.strides, padding=self.padding, data_format=self.data_format, dilation_rate=self.dilation_rate, ) if not tf.executing_eagerly(): # 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=conv_utils.convert_data_format( self.data_format, ndim=4 ), ) 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, h_axis, w_axis = 1, 2, 3 else: c_axis, h_axis, w_axis = 3, 1, 2 kernel_h, kernel_w = self.kernel_size stride_h, stride_w = self.strides if self.output_padding is None: out_pad_h = out_pad_w = None else: out_pad_h, out_pad_w = self.output_padding output_shape[c_axis] = self.filters output_shape[h_axis] = conv_utils.deconv_output_length( output_shape[h_axis], kernel_h, padding=self.padding, output_padding=out_pad_h, stride=stride_h, dilation=self.dilation_rate[0], ) output_shape[w_axis] = conv_utils.deconv_output_length( output_shape[w_axis], kernel_w, padding=self.padding, output_padding=out_pad_w, stride=stride_w, dilation=self.dilation_rate[1], ) return tf.TensorShape(output_shape) def get_config(self): config = super().get_config() config["output_padding"] = self.output_padding return config # Alias Convolution2DTranspose = Conv2DTranspose