368 lines
14 KiB
Python
368 lines
14 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 2D transposed convolution layer (sometimes called deconvolution)."""
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import tensorflow.compat.v2 as tf
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from keras import activations
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from keras import backend
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from keras import constraints
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from keras import initializers
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from keras import regularizers
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from keras.dtensor import utils
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from keras.engine.input_spec import InputSpec
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from keras.layers.convolutional.conv2d import Conv2D
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from keras.utils import conv_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(
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"keras.layers.Conv2DTranspose", "keras.layers.Convolution2DTranspose"
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)
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class Conv2DTranspose(Conv2D):
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"""Transposed convolution layer (sometimes called Deconvolution).
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The need for transposed convolutions generally arises
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from the desire to use a transformation going in the opposite direction
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of a normal convolution, i.e., from something that has the shape of the
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output of some convolution to something that has the shape of its input
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while maintaining a connectivity pattern that is compatible with
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said convolution.
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When using this layer as the first layer in a model,
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provide the keyword argument `input_shape`
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(tuple of integers or `None`, does not include the sample axis),
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e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
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in `data_format="channels_last"`.
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Args:
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filters: Integer, the dimensionality of the output space
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(i.e. the number of output filters in the convolution).
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kernel_size: An integer or tuple/list of 2 integers, specifying the
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height and width of the 2D convolution window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the convolution along the height and width.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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Specifying any stride value != 1 is incompatible with specifying
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any `dilation_rate` value != 1.
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padding: one of `"valid"` or `"same"` (case-insensitive).
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`"valid"` means no padding. `"same"` results in padding with zeros
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evenly to the left/right or up/down of the input such that output has
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the same height/width dimension as the input.
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output_padding: An integer or tuple/list of 2 integers,
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specifying the amount of padding along the height and width
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of the output tensor.
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Can be a single integer to specify the same value for all
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spatial dimensions.
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The amount of output padding along a given dimension must be
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lower than the stride along that same dimension.
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If set to `None` (default), the output shape is inferred.
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape
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`(batch_size, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch_size, channels, height, width)`.
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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "channels_last".
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dilation_rate: an integer, specifying the dilation rate for all spatial
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dimensions for dilated convolution. Specifying different dilation rates
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for different dimensions is not supported.
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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any stride value != 1.
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activation: Activation function to use.
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If you don't specify anything, no activation is applied
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(see `keras.activations`).
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use_bias: Boolean, whether the layer uses a bias vector.
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kernel_initializer: Initializer for the `kernel` weights matrix
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(see `keras.initializers`). Defaults to 'glorot_uniform'.
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bias_initializer: Initializer for the bias vector
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(see `keras.initializers`). Defaults to 'zeros'.
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kernel_regularizer: Regularizer function applied to
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the `kernel` weights matrix (see `keras.regularizers`).
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bias_regularizer: Regularizer function applied to the bias vector
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(see `keras.regularizers`).
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activity_regularizer: Regularizer function applied to
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the output of the layer (its "activation") (see `keras.regularizers`).
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kernel_constraint: Constraint function applied to the kernel matrix
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(see `keras.constraints`).
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bias_constraint: Constraint function applied to the bias vector
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(see `keras.constraints`).
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Input shape:
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4D tensor with shape:
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`(batch_size, channels, rows, cols)` if data_format='channels_first'
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or 4D tensor with shape:
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`(batch_size, rows, cols, channels)` if data_format='channels_last'.
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Output shape:
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4D tensor with shape:
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`(batch_size, filters, new_rows, new_cols)` if
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data_format='channels_first'
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or 4D tensor with shape:
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`(batch_size, new_rows, new_cols, filters)` if
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data_format='channels_last'. `rows` and `cols` values might have changed
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due to padding.
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If `output_padding` is specified:
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```
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new_rows = ((rows - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
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output_padding[0])
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new_cols = ((cols - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
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output_padding[1])
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```
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Returns:
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A tensor of rank 4 representing
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`activation(conv2dtranspose(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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References:
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- [A guide to convolution arithmetic for deep
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learning](https://arxiv.org/abs/1603.07285v1)
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- [Deconvolutional
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Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
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"""
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@utils.allow_initializer_layout
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def __init__(
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self,
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filters,
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kernel_size,
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strides=(1, 1),
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padding="valid",
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output_padding=None,
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data_format=None,
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dilation_rate=(1, 1),
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activation=None,
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use_bias=True,
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kernel_initializer="glorot_uniform",
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bias_initializer="zeros",
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kernel_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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kernel_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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filters=filters,
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kernel_size=kernel_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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dilation_rate=dilation_rate,
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activation=activations.get(activation),
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use_bias=use_bias,
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kernel_initializer=initializers.get(kernel_initializer),
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bias_initializer=initializers.get(bias_initializer),
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kernel_regularizer=regularizers.get(kernel_regularizer),
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bias_regularizer=regularizers.get(bias_regularizer),
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activity_regularizer=regularizers.get(activity_regularizer),
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kernel_constraint=constraints.get(kernel_constraint),
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bias_constraint=constraints.get(bias_constraint),
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**kwargs,
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)
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self.output_padding = output_padding
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if self.output_padding is not None:
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self.output_padding = conv_utils.normalize_tuple(
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self.output_padding, 2, "output_padding", allow_zero=True
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)
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for stride, out_pad in zip(self.strides, self.output_padding):
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if out_pad >= stride:
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raise ValueError(
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"Strides must be greater than output padding. "
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f"Received strides={self.strides}, "
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f"output_padding={self.output_padding}."
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)
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def build(self, input_shape):
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input_shape = tf.TensorShape(input_shape)
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if len(input_shape) != 4:
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raise ValueError(
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"Inputs should have rank 4. "
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f"Received input_shape={input_shape}."
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)
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channel_axis = self._get_channel_axis()
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if input_shape.dims[channel_axis].value is None:
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raise ValueError(
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"The channel dimension of the inputs "
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"to `Conv2DTranspose` should be defined. "
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f"The input_shape received is {input_shape}, "
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f"where axis {channel_axis} (0-based) "
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"is the channel dimension, which found to be `None`."
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)
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input_dim = int(input_shape[channel_axis])
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self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
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kernel_shape = self.kernel_size + (self.filters, input_dim)
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self.kernel = self.add_weight(
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name="kernel",
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shape=kernel_shape,
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initializer=self.kernel_initializer,
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint,
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trainable=True,
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dtype=self.dtype,
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)
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if self.use_bias:
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self.bias = self.add_weight(
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name="bias",
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shape=(self.filters,),
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initializer=self.bias_initializer,
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint,
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trainable=True,
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dtype=self.dtype,
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)
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else:
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self.bias = None
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self.built = True
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def call(self, inputs):
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inputs_shape = tf.shape(inputs)
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batch_size = inputs_shape[0]
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if self.data_format == "channels_first":
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h_axis, w_axis = 2, 3
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else:
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h_axis, w_axis = 1, 2
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# Use the constant height and weight when possible.
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# TODO(scottzhu): Extract this into a utility function that can be
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# applied to all convolutional layers, which currently lost the static
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# shape information due to tf.shape().
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height, width = None, None
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if inputs.shape.rank is not None:
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dims = inputs.shape.as_list()
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height = dims[h_axis]
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width = dims[w_axis]
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height = height if height is not None else inputs_shape[h_axis]
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width = width if width is not None else inputs_shape[w_axis]
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kernel_h, kernel_w = self.kernel_size
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stride_h, stride_w = self.strides
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if self.output_padding is None:
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out_pad_h = out_pad_w = None
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else:
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out_pad_h, out_pad_w = self.output_padding
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# Infer the dynamic output shape:
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out_height = conv_utils.deconv_output_length(
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height,
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kernel_h,
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padding=self.padding,
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output_padding=out_pad_h,
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stride=stride_h,
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dilation=self.dilation_rate[0],
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)
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out_width = conv_utils.deconv_output_length(
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width,
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kernel_w,
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padding=self.padding,
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output_padding=out_pad_w,
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stride=stride_w,
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dilation=self.dilation_rate[1],
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)
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if self.data_format == "channels_first":
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output_shape = (batch_size, self.filters, out_height, out_width)
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else:
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output_shape = (batch_size, out_height, out_width, self.filters)
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output_shape_tensor = tf.stack(output_shape)
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outputs = backend.conv2d_transpose(
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inputs,
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self.kernel,
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output_shape_tensor,
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strides=self.strides,
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padding=self.padding,
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data_format=self.data_format,
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dilation_rate=self.dilation_rate,
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)
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if not tf.executing_eagerly() and inputs.shape.rank:
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# Infer the static output shape:
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out_shape = self.compute_output_shape(inputs.shape)
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outputs.set_shape(out_shape)
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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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if self.activation is not None:
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return self.activation(outputs)
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return outputs
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def compute_output_shape(self, input_shape):
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input_shape = tf.TensorShape(input_shape).as_list()
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output_shape = list(input_shape)
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if self.data_format == "channels_first":
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c_axis, h_axis, w_axis = 1, 2, 3
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else:
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c_axis, h_axis, w_axis = 3, 1, 2
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kernel_h, kernel_w = self.kernel_size
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stride_h, stride_w = self.strides
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if self.output_padding is None:
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out_pad_h = out_pad_w = None
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else:
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out_pad_h, out_pad_w = self.output_padding
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output_shape[c_axis] = self.filters
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output_shape[h_axis] = conv_utils.deconv_output_length(
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output_shape[h_axis],
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kernel_h,
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padding=self.padding,
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output_padding=out_pad_h,
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stride=stride_h,
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dilation=self.dilation_rate[0],
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)
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output_shape[w_axis] = conv_utils.deconv_output_length(
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output_shape[w_axis],
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kernel_w,
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padding=self.padding,
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output_padding=out_pad_w,
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stride=stride_w,
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dilation=self.dilation_rate[1],
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)
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return tf.TensorShape(output_shape)
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def get_config(self):
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config = super().get_config()
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|
config["output_padding"] = self.output_padding
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||
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return config
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||
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||
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# Alias
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||
|
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Convolution2DTranspose = Conv2DTranspose
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