Opus ng lace

dnn/torch/osce/models/scale_embedding.py

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