58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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from keras_core.layers.input_spec import InputSpec
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from keras_core.layers.layer import Layer
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@keras_core_export("keras_core.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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Args:
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dims: Tuple of integers. Permutation pattern does not include the
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batch 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.
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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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Example:
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>>> x = keras_core.Input(shape=(10, 64))
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>>> y = keras_core.layers.Permute((2, 1))(x)
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>>> y.shape
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(None, 64, 10)
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"""
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def __init__(self, dims, name=None, dtype=None):
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super().__init__(name=name, dtype=dtype)
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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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output_shape = [input_shape[0]]
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for dim in self.dims:
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output_shape.append(input_shape[dim])
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return tuple(output_shape)
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def call(self, inputs):
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return ops.transpose(inputs, axes=(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 {**base_config, **config}
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