Update activations.py

utils/activations.py

@@ -58,36 +58,39 @@ def forward(self, x):
 # ACON https://arxiv.org/pdf/2009.04759.pdf ----------------------------------------------------------------------------
 class AconC(nn.Module):
     r""" ACON activation (activate or not).
-    # AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
-    # according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
+    AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
+    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
     """
 
-    def __init__(self, width):
+    def __init__(self, c1):
         super().__init__()
-        self.p1 = nn.Parameter(torch.randn(1, width, 1, 1))
-        self.p2 = nn.Parameter(torch.randn(1, width, 1, 1))
-        self.beta = nn.Parameter(torch.ones(1, width, 1, 1))
+        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
+        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
+        self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
 
     def forward(self, x):
-        return (self.p1 * x - self.p2 * x) * torch.sigmoid(self.beta * (self.p1 * x - self.p2 * x)) + self.p2 * x
+        dpx = (self.p1 - self.p2) * x
+        return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
 
 
 class MetaAconC(nn.Module):
     r""" ACON activation (activate or not).
-    # MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
-    # according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
+    MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
+    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
     """
 
-    def __init__(self, width, r=16):
+    def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
         super().__init__()
-        self.p1 = nn.Parameter(torch.randn(1, width, 1, 1))
-        self.p2 = nn.Parameter(torch.randn(1, width, 1, 1))
-        self.fc1 = nn.Conv2d(width, max(r, width // r), kernel_size=1, stride=1, bias=True)
-        self.bn1 = nn.BatchNorm2d(max(r, width // r))
-        self.fc2 = nn.Conv2d(max(r, width // r), width, kernel_size=1, stride=1, bias=True)
-        self.bn2 = nn.BatchNorm2d(width)
+        c2 = max(r, c1 // r)
+        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
+        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
+        self.fc1 = nn.Conv2d(c1, c2, k, s, bias=False)
+        self.bn1 = nn.BatchNorm2d(c2)
+        self.fc2 = nn.Conv2d(c2, c1, k, s, bias=False)
+        self.bn2 = nn.BatchNorm2d(c1)
 
     def forward(self, x):
-        beta = torch.sigmoid(
-            self.bn2(self.fc2(self.bn1(self.fc1(x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True))))))
-        return (self.p1 * x - self.p2 * x) * torch.sigmoid(beta * (self.p1 * x - self.p2 * x)) + self.p2 * x
+        y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
+        beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))
+        dpx = (self.p1 - self.p2) * x
+        return dpx * torch.sigmoid(beta * dpx) + self.p2 * x