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74 lines
2.0 KiB
74 lines
2.0 KiB
2 years ago
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"""Library implementing linear transformation.
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Authors
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* Mirco Ravanelli 2020
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* Davide Borra 2021
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"""
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import logging
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import paddle
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import paddle.nn as nn
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from paddlespeech.s2t.modules import align
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logger = logging.getLogger(__name__)
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class Linear(paddle.nn.Layer):
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"""Computes a linear transformation y = wx + b.
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Arguments
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---------
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n_neurons : int
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It is the number of output neurons (i.e, the dimensionality of the
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output).
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input_shape: tuple
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It is the shape of the input tensor.
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input_size: int
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Size of the input tensor.
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bias : bool
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If True, the additive bias b is adopted.
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combine_dims : bool
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If True and the input is 4D, combine 3rd and 4th dimensions of input.
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Example
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-------
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>>> inputs = paddle.rand(10, 50, 40)
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>>> lin_t = Linear(input_shape=(10, 50, 40), n_neurons=100)
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>>> output = lin_t(inputs)
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>>> output.shape
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paddle.shape([10, 50, 100])
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"""
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def __init__(
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self,
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n_neurons,
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input_shape=None,
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input_size=None,
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bias=True,
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combine_dims=False,
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):
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super().__init__()
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self.combine_dims = combine_dims
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if input_shape is None and input_size is None:
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raise ValueError("Expected one of input_shape or input_size")
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if input_size is None:
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input_size = input_shape[-1]
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if len(input_shape) == 4 and self.combine_dims:
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input_size = input_shape[2] * input_shape[3]
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# Weights are initialized following paddle approach
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self.w = align.Linear(input_size, n_neurons, bias_attr=bias)
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def forward(self, x):
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"""Returns the linear transformation of input tensor.
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Arguments
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---------
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x : paddle.Tensor
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Input to transform linearly.
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"""
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if x.rank == 4 and self.combine_dims:
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x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3])
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wx = self.w(x)
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return wx
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