249 lines
11 KiB
Python
249 lines
11 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 abstract base layer for separable nD convolution."""
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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 constraints
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from keras import initializers
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from keras import regularizers
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from keras.engine.input_spec import InputSpec
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from keras.layers.convolutional.base_conv import Conv
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class SeparableConv(Conv):
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"""Abstract base layer for separable nD convolution.
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This layer performs a depthwise convolution that acts separately on
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channels, followed by a pointwise convolution that mixes channels.
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If `use_bias` is True and a bias initializer is provided,
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it adds a bias vector to the output.
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It then optionally applies an activation function to produce the final
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output.
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Args:
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rank: An integer, the rank of the convolution, e.g. "2" for 2D
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convolution.
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filters: Integer, the dimensionality of the output space (i.e. the number
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of filters in the convolution).
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kernel_size: A tuple or list of integers specifying the spatial
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dimensions of the filters. Can be a single integer to specify the same
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value for all spatial dimensions.
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strides: A tuple or list of integers specifying the strides
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of the convolution. Can be a single integer to specify the same value
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for 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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data_format: A string, one of `channels_last` (default) or
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`channels_first`. 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, ..., channels)` while `channels_first` corresponds to
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inputs with shape `(batch_size, channels, ...)`.
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dilation_rate: An integer or tuple/list of 2 integers, specifying
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the dilation rate to use for dilated convolution.
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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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Currently, specifying any `dilation_rate` value != 1 is
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incompatible with specifying any stride value != 1.
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depth_multiplier: The number of depthwise convolution output channels for
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each input channel. The total number of depthwise convolution output
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channels will be equal to `num_filters_in * depth_multiplier`.
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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.
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depthwise_initializer: An initializer for the depthwise convolution kernel
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(see `keras.initializers`). If None, then the default initializer
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('glorot_uniform') will be used.
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pointwise_initializer: An initializer for the pointwise convolution kernel
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(see `keras.initializers`). If None, then the default initializer
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('glorot_uniform') will be used.
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bias_initializer: An initializer for the bias vector. If None, the default
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initializer ('zeros') will be used (see `keras.initializers`).
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depthwise_regularizer: Optional regularizer for the depthwise
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convolution kernel.
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pointwise_regularizer: Optional regularizer for the pointwise
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convolution kernel.
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bias_regularizer: Optional regularizer for the bias vector.
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activity_regularizer: Optional regularizer function for the output.
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depthwise_constraint: Optional projection function to be applied to the
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depthwise kernel after being updated by an `Optimizer` (e.g. used for
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norm constraints or value constraints for layer weights). The function
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must take as input the unprojected variable and must return the
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projected variable (which must have the same shape). Constraints are
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not safe to use when doing asynchronous distributed training.
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pointwise_constraint: Optional projection function to be applied to the
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pointwise kernel after being updated by an `Optimizer`.
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bias_constraint: Optional projection function to be applied to the
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bias after being updated by an `Optimizer`.
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trainable: Boolean, if `True` the weights of this layer will be marked as
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trainable (and listed in `layer.trainable_weights`).
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"""
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def __init__(
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self,
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rank,
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filters,
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kernel_size,
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strides=1,
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padding="valid",
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data_format=None,
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dilation_rate=1,
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depth_multiplier=1,
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activation=None,
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use_bias=True,
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depthwise_initializer="glorot_uniform",
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pointwise_initializer="glorot_uniform",
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bias_initializer="zeros",
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depthwise_regularizer=None,
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pointwise_regularizer=None,
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bias_regularizer=None,
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activity_regularizer=None,
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depthwise_constraint=None,
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pointwise_constraint=None,
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bias_constraint=None,
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trainable=True,
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name=None,
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**kwargs,
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):
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super().__init__(
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rank=rank,
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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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bias_initializer=initializers.get(bias_initializer),
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bias_regularizer=regularizers.get(bias_regularizer),
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activity_regularizer=regularizers.get(activity_regularizer),
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bias_constraint=bias_constraint,
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trainable=trainable,
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name=name,
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**kwargs,
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)
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self.depth_multiplier = depth_multiplier
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self.depthwise_initializer = initializers.get(depthwise_initializer)
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self.pointwise_initializer = initializers.get(pointwise_initializer)
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self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
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self.pointwise_regularizer = regularizers.get(pointwise_regularizer)
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self.depthwise_constraint = constraints.get(depthwise_constraint)
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self.pointwise_constraint = constraints.get(pointwise_constraint)
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def build(self, input_shape):
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input_shape = tf.TensorShape(input_shape)
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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 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(
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ndim=self.rank + 2, axes={channel_axis: input_dim}
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)
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depthwise_kernel_shape = self.kernel_size + (
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input_dim,
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self.depth_multiplier,
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)
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pointwise_kernel_shape = (1,) * self.rank + (
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self.depth_multiplier * input_dim,
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self.filters,
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)
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self.depthwise_kernel = self.add_weight(
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name="depthwise_kernel",
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shape=depthwise_kernel_shape,
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initializer=self.depthwise_initializer,
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regularizer=self.depthwise_regularizer,
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constraint=self.depthwise_constraint,
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trainable=True,
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dtype=self.dtype,
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)
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self.pointwise_kernel = self.add_weight(
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name="pointwise_kernel",
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shape=pointwise_kernel_shape,
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initializer=self.pointwise_initializer,
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regularizer=self.pointwise_regularizer,
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constraint=self.pointwise_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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raise NotImplementedError
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def get_config(self):
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config = {
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"filters": self.filters,
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"kernel_size": self.kernel_size,
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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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"depth_multiplier": self.depth_multiplier,
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"dilation_rate": self.dilation_rate,
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"activation": activations.serialize(self.activation),
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"use_bias": self.use_bias,
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"depthwise_initializer": initializers.serialize(
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self.depthwise_initializer
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),
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"pointwise_initializer": initializers.serialize(
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self.pointwise_initializer
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),
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"bias_initializer": initializers.serialize(self.bias_initializer),
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"depthwise_regularizer": regularizers.serialize(
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self.depthwise_regularizer
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),
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"pointwise_regularizer": regularizers.serialize(
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self.pointwise_regularizer
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),
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"bias_regularizer": regularizers.serialize(self.bias_regularizer),
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"activity_regularizer": regularizers.serialize(
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self.activity_regularizer
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),
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"depthwise_constraint": constraints.serialize(
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self.depthwise_constraint
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),
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"pointwise_constraint": constraints.serialize(
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self.pointwise_constraint
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),
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"bias_constraint": constraints.serialize(self.bias_constraint),
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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