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models/encoder.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ConvLayer(nn.Module):
+    def __init__(self, c_in):
+        super(ConvLayer, self).__init__()
+        padding = 1 if torch.__version__>='1.5.0' else 2
+        self.downConv = nn.Conv1d(in_channels=c_in,
+                                  out_channels=c_in,
+                                  kernel_size=3,
+                                  padding=padding,
+                                  padding_mode='circular')
+        self.norm = nn.BatchNorm1d(c_in)
+        self.activation = nn.ELU()
+        self.maxPool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
+
+    def forward(self, x):
+        x = self.downConv(x.permute(0, 2, 1))
+        x = self.norm(x)
+        x = self.activation(x)
+        x = self.maxPool(x)
+        x = x.transpose(1,2)
+        return x
+
+class EncoderLayer(nn.Module):
+    def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
+        super(EncoderLayer, self).__init__()
+        d_ff = d_ff or 4*d_model
+        self.attention = 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.dropout = nn.Dropout(dropout)
+        self.activation = F.relu if activation == "relu" else F.gelu
+
+    def forward(self, x, attn_mask=None):
+        # x [B, L, D]
+        # x = x + self.dropout(self.attention(
+        #     x, x, x,
+        #     attn_mask = attn_mask
+        # ))
+        new_x, attn = self.attention(
+            x, x, x,
+            attn_mask = attn_mask
+        )
+        x = x + self.dropout(new_x)
+
+        y = x = self.norm1(x)
+        y = self.dropout(self.activation(self.conv1(y.transpose(-1,1))))
+        y = self.dropout(self.conv2(y).transpose(-1,1))
+
+        return self.norm2(x+y), attn
+
+class Encoder(nn.Module):
+    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
+        super(Encoder, self).__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None):
+        # x [B, L, D]
+        attns = []
+        if self.conv_layers is not None:
+            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
+                x, attn = attn_layer(x, attn_mask=attn_mask)
+                x = conv_layer(x)
+                attns.append(attn)
+            x, attn = self.attn_layers[-1](x, attn_mask=attn_mask)
+            attns.append(attn)
+        else:
+            for attn_layer in self.attn_layers:
+                x, attn = attn_layer(x, attn_mask=attn_mask)
+                attns.append(attn)
+
+        if self.norm is not None:
+            x = self.norm(x)
+
+        return x, attns
+
+class EncoderStack(nn.Module):
+    def __init__(self, encoders, inp_lens):
+        super(EncoderStack, self).__init__()
+        self.encoders = nn.ModuleList(encoders)
+        self.inp_lens = inp_lens
+
+    def forward(self, x, attn_mask=None):
+        # x [B, L, D]
+        x_stack = []; attns = []
+        for i_len, encoder in zip(self.inp_lens, self.encoders):
+            inp_len = x.shape[1]//(2**i_len)
+            x_s, attn = encoder(x[:, -inp_len:, :])
+            x_stack.append(x_s); attns.append(attn)
+        x_stack = torch.cat(x_stack, -2)
+        
+        return x_stack, attns