Refactor Project to 3 parts: Models, Control, Data

Need readme

models/ppg_extractor/encoder/embedding.py

@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Copyright 2019 Shigeki Karita
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""Positonal Encoding Module."""
+
+import math
+
+import torch
+
+
+def _pre_hook(
+    state_dict,
+    prefix,
+    local_metadata,
+    strict,
+    missing_keys,
+    unexpected_keys,
+    error_msgs,
+):
+    """Perform pre-hook in load_state_dict for backward compatibility.
+
+    Note:
+        We saved self.pe until v.0.5.2 but we have omitted it later.
+        Therefore, we remove the item "pe" from `state_dict` for backward compatibility.
+
+    """
+    k = prefix + "pe"
+    if k in state_dict:
+        state_dict.pop(k)
+
+
+class PositionalEncoding(torch.nn.Module):
+    """Positional encoding.
+
+    :param int d_model: embedding dim
+    :param float dropout_rate: dropout rate
+    :param int max_len: maximum input length
+    :param reverse: whether to reverse the input position
+
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
+        """Construct an PositionalEncoding object."""
+        super(PositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.reverse = reverse
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, max_len))
+        self._register_load_state_dict_pre_hook(_pre_hook)
+
+    def extend_pe(self, x):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1):
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        pe = torch.zeros(x.size(1), self.d_model)
+        if self.reverse:
+            position = torch.arange(
+                x.size(1) - 1, -1, -1.0, dtype=torch.float32
+            ).unsqueeze(1)
+        else:
+            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.d_model, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.d_model)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype)
+
+    def forward(self, x: torch.Tensor):
+        """Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input. Its shape is (batch, time, ...)
+
+        Returns:
+            torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
+
+        """
+        self.extend_pe(x)
+        x = x * self.xscale + self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class ScaledPositionalEncoding(PositionalEncoding):
+    """Scaled positional encoding module.
+
+    See also: Sec. 3.2  https://arxiv.org/pdf/1809.08895.pdf
+
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """Initialize class.
+
+        :param int d_model: embedding dim
+        :param float dropout_rate: dropout rate
+        :param int max_len: maximum input length
+
+        """
+        super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
+        self.alpha = torch.nn.Parameter(torch.tensor(1.0))
+
+    def reset_parameters(self):
+        """Reset parameters."""
+        self.alpha.data = torch.tensor(1.0)
+
+    def forward(self, x):
+        """Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input. Its shape is (batch, time, ...)
+
+        Returns:
+            torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
+
+        """
+        self.extend_pe(x)
+        x = x + self.alpha * self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class RelPositionalEncoding(PositionalEncoding):
+    """Relitive positional encoding module.
+
+    See : Appendix B in https://arxiv.org/abs/1901.02860
+
+    :param int d_model: embedding dim
+    :param float dropout_rate: dropout rate
+    :param int max_len: maximum input length
+
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """Initialize class.
+
+        :param int d_model: embedding dim
+        :param float dropout_rate: dropout rate
+        :param int max_len: maximum input length
+
+        """
+        super().__init__(d_model, dropout_rate, max_len, reverse=True)
+
+    def forward(self, x):
+        """Compute positional encoding.
+
+        Args:
+            x (torch.Tensor): Input. Its shape is (batch, time, ...)
+
+        Returns:
+            torch.Tensor: x. Its shape is (batch, time, ...)
+            torch.Tensor: pos_emb. Its shape is (1, time, ...)
+
+        """
+        self.extend_pe(x)
+        x = x * self.xscale
+        pos_emb = self.pe[:, : x.size(1)]
+        return self.dropout(x), self.dropout(pos_emb)