Add causal person fusion and MuVAP stage-3 baselines

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

@@ -0,0 +1,53 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
+                 dropout=0.1, activation="relu"):
+        super(DecoderLayer, self).__init__()
+        d_ff = d_ff or 4*d_model
+        self.self_attention = self_attention
+        self.cross_attention = cross_attention
+        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
+        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None):
+        x = x + self.dropout(self.self_attention(
+            x, x, x,
+            attn_mask=x_mask
+        ))
+        x = self.norm1(x)
+
+        x = x + self.dropout(self.cross_attention(
+            x, cross, cross,
+            attn_mask=cross_mask
+        ))
+
+        y = x = self.norm2(x)
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1,1))))
+        y = self.dropout(self.conv2(y).transpose(-1,1))
+
+        return self.norm3(x+y)
+
+
+class Decoder(nn.Module):
+    def __init__(self, layers, norm_layer=None):
+        super(Decoder, self).__init__()
+        self.layers = nn.ModuleList(layers)
+        self.norm = norm_layer
+
+    def forward(self, x, cross, x_mask=None, cross_mask=None):
+        for layer in self.layers:
+            x = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x