371 lines
15 KiB
Python
371 lines
15 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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"""Locally-connected layer for 1D input."""
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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.engine.base_layer import Layer
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from keras.engine.input_spec import InputSpec
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from keras.layers.locally_connected import locally_connected_utils
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from keras.utils import conv_utils
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from keras.utils import tf_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("keras.layers.LocallyConnected1D")
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class LocallyConnected1D(Layer):
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"""Locally-connected layer for 1D inputs.
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The `LocallyConnected1D` layer works similarly to
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the `Conv1D` layer, except that weights are unshared,
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that is, a different set of filters is applied at each different patch
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of the input.
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Note: layer attributes cannot be modified after the layer has been called
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once (except the `trainable` attribute).
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Example:
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```python
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# apply a unshared weight convolution 1d of length 3 to a sequence with
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# 10 timesteps, with 64 output filters
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model = Sequential()
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model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
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# now model.output_shape == (None, 8, 64)
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# add a new conv1d on top
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model.add(LocallyConnected1D(32, 3))
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# now model.output_shape == (None, 6, 32)
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```
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Args:
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filters: Integer, the dimensionality of the output space (i.e. the
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number of output filters in the convolution).
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kernel_size: An integer or tuple/list of a single integer, specifying
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the length of the 1D convolution window.
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strides: An integer or tuple/list of a single integer, specifying the
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stride length of the convolution.
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padding: Currently only supports `"valid"` (case-insensitive). `"same"`
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may be supported in the future. `"valid"` means no padding.
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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 `(batch, length,
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channels)` while `channels_first` corresponds to inputs with shape
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`(batch, channels, length)`. It defaults to the `image_data_format`
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value found in your Keras config file at `~/.keras/keras.json`. If you
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never set it, then it will be "channels_last".
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activation: Activation function to use. If you don't specify anything,
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no activation is applied (ie. "linear" activation: `a(x) = x`).
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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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bias_initializer: Initializer for the bias vector.
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kernel_regularizer: Regularizer function applied to the `kernel` weights
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matrix.
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bias_regularizer: Regularizer function applied to the bias vector.
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activity_regularizer: Regularizer function applied to the output of the
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layer (its "activation")..
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kernel_constraint: Constraint function applied to the kernel matrix.
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bias_constraint: Constraint function applied to the bias vector.
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implementation: implementation mode, either `1`, `2`, or `3`. `1` loops
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over input spatial locations to perform the forward pass. It is
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memory-efficient but performs a lot of (small) ops. `2` stores layer
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weights in a dense but sparsely-populated 2D matrix and implements the
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forward pass as a single matrix-multiply. It uses a lot of RAM but
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performs few (large) ops. `3` stores layer weights in a sparse tensor
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and implements the forward pass as a single sparse matrix-multiply.
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How to choose:
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`1`: large, dense models,
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`2`: small models,
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`3`: large, sparse models, where "large" stands for large
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input/output activations (i.e. many `filters`, `input_filters`,
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large `input_size`, `output_size`), and "sparse" stands for few
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connections between inputs and outputs, i.e. small ratio
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`filters * input_filters * kernel_size / (input_size * strides)`,
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where inputs to and outputs of the layer are assumed to have
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shapes `(input_size, input_filters)`, `(output_size, filters)`
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respectively. It is recommended to benchmark each in the setting
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of interest to pick the most efficient one (in terms of speed and
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memory usage). Correct choice of implementation can lead to
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dramatic speed improvements (e.g. 50X), potentially at the expense
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of RAM. Also, only `padding="valid"` is supported by
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`implementation=1`.
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Input shape:
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3D tensor with shape: `(batch_size, steps, input_dim)`
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Output shape:
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3D tensor with shape: `(batch_size, new_steps, filters)` `steps` value
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might have changed due to padding or strides.
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"""
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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,
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padding="valid",
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data_format=None,
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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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implementation=1,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.filters = filters
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self.kernel_size = conv_utils.normalize_tuple(
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kernel_size, 1, "kernel_size"
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)
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self.strides = conv_utils.normalize_tuple(
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strides, 1, "strides", allow_zero=True
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)
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self.padding = conv_utils.normalize_padding(padding)
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if self.padding != "valid" and implementation == 1:
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raise ValueError(
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"Invalid border mode for LocallyConnected1D "
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'(only "valid" is supported if implementation is 1): ' + padding
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)
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self.data_format = conv_utils.normalize_data_format(data_format)
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self.activation = activations.get(activation)
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self.use_bias = use_bias
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self.kernel_initializer = initializers.get(kernel_initializer)
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self.bias_initializer = initializers.get(bias_initializer)
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self.kernel_regularizer = regularizers.get(kernel_regularizer)
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self.bias_regularizer = regularizers.get(bias_regularizer)
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self.activity_regularizer = regularizers.get(activity_regularizer)
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self.kernel_constraint = constraints.get(kernel_constraint)
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self.bias_constraint = constraints.get(bias_constraint)
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self.implementation = implementation
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self.input_spec = InputSpec(ndim=3)
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@property
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def _use_input_spec_as_call_signature(self):
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return False
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@tf_utils.shape_type_conversion
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def build(self, input_shape):
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if self.data_format == "channels_first":
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input_dim, input_length = input_shape[1], input_shape[2]
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else:
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input_dim, input_length = input_shape[2], input_shape[1]
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if input_dim is None:
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raise ValueError(
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"Axis 2 of input should be fully-defined. Found shape:",
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input_shape,
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)
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self.output_length = conv_utils.conv_output_length(
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input_length, self.kernel_size[0], self.padding, self.strides[0]
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)
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if self.output_length <= 0:
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raise ValueError(
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"One of the dimensions in the output is <= 0 "
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f"due to downsampling in {self.name}. Consider "
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"increasing the input size. "
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f"Received input shape {input_shape} which would produce "
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"output shape with a zero or negative value in a "
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"dimension."
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)
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if self.implementation == 1:
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self.kernel_shape = (
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self.output_length,
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self.kernel_size[0] * input_dim,
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self.filters,
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)
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self.kernel = self.add_weight(
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shape=self.kernel_shape,
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initializer=self.kernel_initializer,
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name="kernel",
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint,
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)
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elif self.implementation == 2:
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if self.data_format == "channels_first":
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self.kernel_shape = (
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input_dim,
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input_length,
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self.filters,
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self.output_length,
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)
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else:
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self.kernel_shape = (
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input_length,
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input_dim,
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self.output_length,
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self.filters,
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)
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self.kernel = self.add_weight(
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shape=self.kernel_shape,
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initializer=self.kernel_initializer,
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name="kernel",
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint,
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)
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self.kernel_mask = (
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locally_connected_utils.get_locallyconnected_mask(
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input_shape=(input_length,),
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kernel_shape=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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)
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)
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elif self.implementation == 3:
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self.kernel_shape = (
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self.output_length * self.filters,
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input_length * input_dim,
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)
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self.kernel_idxs = sorted(
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conv_utils.conv_kernel_idxs(
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input_shape=(input_length,),
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kernel_shape=self.kernel_size,
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strides=self.strides,
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padding=self.padding,
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filters_in=input_dim,
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filters_out=self.filters,
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data_format=self.data_format,
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)
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)
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self.kernel = self.add_weight(
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shape=(len(self.kernel_idxs),),
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initializer=self.kernel_initializer,
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name="kernel",
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regularizer=self.kernel_regularizer,
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constraint=self.kernel_constraint,
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)
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else:
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raise ValueError(
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"Unrecognized implementation mode: %d." % self.implementation
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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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shape=(self.output_length, self.filters),
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initializer=self.bias_initializer,
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name="bias",
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regularizer=self.bias_regularizer,
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constraint=self.bias_constraint,
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)
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else:
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self.bias = None
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if self.data_format == "channels_first":
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self.input_spec = InputSpec(ndim=3, axes={1: input_dim})
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else:
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self.input_spec = InputSpec(ndim=3, axes={-1: input_dim})
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self.built = True
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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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if self.data_format == "channels_first":
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input_length = input_shape[2]
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else:
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input_length = input_shape[1]
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length = conv_utils.conv_output_length(
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input_length, self.kernel_size[0], self.padding, self.strides[0]
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)
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if self.data_format == "channels_first":
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return (input_shape[0], self.filters, length)
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elif self.data_format == "channels_last":
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return (input_shape[0], length, self.filters)
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def call(self, inputs):
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if self.implementation == 1:
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output = backend.local_conv(
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inputs,
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self.kernel,
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self.kernel_size,
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self.strides,
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(self.output_length,),
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self.data_format,
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)
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elif self.implementation == 2:
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output = locally_connected_utils.local_conv_matmul(
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inputs,
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self.kernel,
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self.kernel_mask,
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self.compute_output_shape(inputs.shape),
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)
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elif self.implementation == 3:
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output = locally_connected_utils.local_conv_sparse_matmul(
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inputs,
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self.kernel,
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self.kernel_idxs,
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self.kernel_shape,
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self.compute_output_shape(inputs.shape),
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)
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else:
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raise ValueError(
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"Unrecognized implementation mode: %d." % self.implementation
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)
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if self.use_bias:
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output = backend.bias_add(
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output, self.bias, data_format=self.data_format
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)
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output = self.activation(output)
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return output
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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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"activation": activations.serialize(self.activation),
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"use_bias": self.use_bias,
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"kernel_initializer": initializers.serialize(
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self.kernel_initializer
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),
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"bias_initializer": initializers.serialize(self.bias_initializer),
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"kernel_regularizer": regularizers.serialize(
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self.kernel_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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"kernel_constraint": constraints.serialize(self.kernel_constraint),
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"bias_constraint": constraints.serialize(self.bias_constraint),
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"implementation": self.implementation,
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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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