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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 torch |
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import torch.nn as nn |
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class Linear(torch.nn.Module): |
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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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max_norm : float |
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weight max-norm. |
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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 = torch.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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torch.Size([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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max_norm=None, |
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combine_dims=False, |
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): |
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super().__init__() |
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self.max_norm = max_norm |
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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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self.w = nn.Linear(input_size, n_neurons, bias=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 : torch.Tensor |
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Input to transform linearly. |
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Returns |
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------- |
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wx : torch.Tensor |
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The linearly transformed outputs. |
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""" |
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if x.ndim == 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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if self.max_norm is not None: |
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self.w.weight.data = torch.renorm( |
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self.w.weight.data, p=2, dim=0, maxnorm=self.max_norm |
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) |
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wx = self.w(x) |
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return wx |
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