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68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
"""
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/* Copyright (c) 2023 Amazon
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Written by Jan Buethe */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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"""
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import math as m
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import torch
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from torch import nn
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class ScaleEmbedding(nn.Module):
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def __init__(self,
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dim,
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min_val,
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max_val,
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logscale=False):
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super().__init__()
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if min_val >= max_val:
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raise ValueError('min_val must be smaller than max_val')
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if min_val <= 0 and logscale:
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raise ValueError('min_val must be positive when logscale is true')
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self.dim = dim
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self.logscale = logscale
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self.min_val = min_val
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self.max_val = max_val
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if logscale:
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self.min_val = m.log(self.min_val)
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self.max_val = m.log(self.max_val)
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self.offset = (self.min_val + self.max_val) / 2
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self.scale_factors = nn.Parameter(
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torch.arange(1, dim+1, dtype=torch.float32) * torch.pi / (self.max_val - self.min_val)
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)
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def forward(self, x):
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if self.logscale: x = torch.log(x)
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x = torch.clip(x, self.min_val, self.max_val) - self.offset
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return torch.sin(x.unsqueeze(-1) * self.scale_factors - 0.5)
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