Add files via upload

2023-03-31 05:16:36 -05:00
parent 786bb05c5e
commit 0c9628d728
3 changed files with 214 additions and 0 deletions
--- a/lib_v5/modules.py
+++ b/lib_v5/modules.py
@@ -0,0 +1,74 @@
+import torch
+import torch.nn as nn
+
+
+class TFC(nn.Module):
+    def __init__(self, c, l, k, norm):
+        super(TFC, self).__init__()
+
+        self.H = nn.ModuleList()
+        for i in range(l):
+            self.H.append(
+                nn.Sequential(
+                    nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),
+                    norm(c),
+                    nn.ReLU(),
+                )
+            )
+
+    def forward(self, x):
+        for h in self.H:
+            x = h(x)
+        return x
+
+
+class DenseTFC(nn.Module):
+    def __init__(self, c, l, k, norm):
+        super(DenseTFC, self).__init__()
+
+        self.conv = nn.ModuleList()
+        for i in range(l):
+            self.conv.append(
+                nn.Sequential(
+                    nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k // 2),
+                    norm(c),
+                    nn.ReLU(),
+                )
+            )
+
+    def forward(self, x):
+        for layer in self.conv[:-1]:
+            x = torch.cat([layer(x), x], 1)
+        return self.conv[-1](x)
+
+
+class TFC_TDF(nn.Module):
+    def __init__(self, c, l, f, k, bn, dense=False, bias=True, norm=nn.BatchNorm2d):
+
+        super(TFC_TDF, self).__init__()
+
+        self.use_tdf = bn is not None
+
+        self.tfc = DenseTFC(c, l, k, norm) if dense else TFC(c, l, k, norm)
+
+        if self.use_tdf:
+            if bn == 0:
+                self.tdf = nn.Sequential(
+                    nn.Linear(f, f, bias=bias),
+                    norm(c),
+                    nn.ReLU()
+                )
+            else:
+                self.tdf = nn.Sequential(
+                    nn.Linear(f, f // bn, bias=bias),
+                    norm(c),
+                    nn.ReLU(),
+                    nn.Linear(f // bn, f, bias=bias),
+                    norm(c),
+                    nn.ReLU()
+                )
+
+    def forward(self, x):
+        x = self.tfc(x)
+        return x + self.tdf(x) if self.use_tdf else x
+