Add causal person fusion and MuVAP stage-3 baselines

third_party/EgoSpeak/MiniROAD/model/transformer_models/PositionalEncoding.py

@@ -0,0 +1,44 @@
+import torch
+import torch.nn as nn
+
+
+class FixedPositionalEncoding(nn.Module):
+    def __init__(self, embedding_dim, max_length=5000):
+        super(FixedPositionalEncoding, self).__init__()
+
+        pe = torch.zeros(max_length, embedding_dim)
+        position = torch.arange(0, max_length, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, embedding_dim, 2).float()
+            * (-torch.log(torch.tensor(10000.0)) / embedding_dim)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0).transpose(0, 1)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[: x.size(0), :]
+        return x
+
+
+class LearnedPositionalEncoding(nn.Module):
+    def __init__(self, max_position_embeddings, embedding_dim, seq_length):
+        super(LearnedPositionalEncoding, self).__init__()
+        self.pe = nn.Embedding(max_position_embeddings, embedding_dim)
+        self.seq_length = seq_length
+
+        self.register_buffer(
+            "position_ids",
+            torch.arange(max_position_embeddings).expand((1, -1)),
+        )
+
+    def forward(self, x, position_ids=None):
+        if position_ids is None:
+            position_ids = self.position_ids[:, : self.seq_length]
+
+        try:
+            position_embeddings = self.pe(position_ids)
+            return x + position_embeddings
+        except:
+            import pdb; pdb.set_trace()