86 lines
2.9 KiB
Python
86 lines
2.9 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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"""Contains the Permute layer."""
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import copy
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import tensorflow.compat.v2 as tf
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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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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.Permute")
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class Permute(Layer):
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"""Permutes the dimensions of the input according to a given pattern.
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Useful e.g. connecting RNNs and convnets.
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Example:
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```python
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model = Sequential()
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model.add(Permute((2, 1), input_shape=(10, 64)))
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# now: model.output_shape == (None, 64, 10)
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# note: `None` is the batch dimension
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```
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Args:
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dims: Tuple of integers. Permutation pattern does not include the
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samples dimension. Indexing starts at 1.
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For instance, `(2, 1)` permutes the first and second dimensions
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of the input.
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Input shape:
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Arbitrary. Use the keyword argument `input_shape`
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(tuple of integers, does not include the samples axis)
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when using this layer as the first layer in a model.
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Output shape:
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Same as the input shape, but with the dimensions re-ordered according
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to the specified pattern.
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"""
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def __init__(self, dims, **kwargs):
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super().__init__(**kwargs)
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self.dims = tuple(dims)
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if sorted(dims) != list(range(1, len(dims) + 1)):
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raise ValueError(
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"Invalid permutation argument `dims` for Permute Layer. "
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"The set of indices in `dims` must be consecutive and start "
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f"from 1. Received dims={dims}"
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)
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self.input_spec = InputSpec(ndim=len(self.dims) + 1)
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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 = copy.copy(input_shape)
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for i, dim in enumerate(self.dims):
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target_dim = input_shape[dim]
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output_shape[i + 1] = target_dim
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return tf.TensorShape(output_shape)
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def call(self, inputs):
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return tf.transpose(inputs, perm=(0,) + self.dims)
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def get_config(self):
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config = {"dims": self.dims}
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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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