update activations.py

utils/activations.py

@@ -3,69 +3,65 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 
-# Swish ------------------------------------------------------------------------
-class SwishImplementation(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        ctx.save_for_backward(x)
-        return x * torch.sigmoid(x)
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        x = ctx.saved_tensors[0]
-        sx = torch.sigmoid(x)
-        return grad_output * (sx * (1 + x * (1 - sx)))
-
-
-class MemoryEfficientSwish(nn.Module):
+# Swish https://arxiv.org/pdf/1905.02244.pdf ---------------------------------------------------------------------------
+class Swish(nn.Module):  #
     @staticmethod
     def forward(x):
-        return SwishImplementation.apply(x)
+        return x * torch.sigmoid(x)
 
 
-class HardSwish(nn.Module):  # https://arxiv.org/pdf/1905.02244.pdf
+class HardSwish(nn.Module):
     @staticmethod
     def forward(x):
         return x * F.hardtanh(x + 3, 0., 6., True) / 6.
 
 
-class Swish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
+class MemoryEfficientSwish(nn.Module):
+    class F(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            return x * torch.sigmoid(x)
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_tensors[0]
+            sx = torch.sigmoid(x)
+            return grad_output * (sx * (1 + x * (1 - sx)))
+
+    def forward(self, x):
+        return self.F.apply(x)
 
 
-# Mish ------------------------------------------------------------------------
-class MishImplementation(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x):
-        ctx.save_for_backward(x)
-        return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        x = ctx.saved_tensors[0]
-        sx = torch.sigmoid(x)
-        fx = F.softplus(x).tanh()
-        return grad_output * (fx + x * sx * (1 - fx * fx))
-
-
-class MemoryEfficientMish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return MishImplementation.apply(x)
-
-
-class Mish(nn.Module):  # https://github.com/digantamisra98/Mish
+# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
+class Mish(nn.Module):
     @staticmethod
     def forward(x):
         return x * F.softplus(x).tanh()
 
 
-# FReLU https://arxiv.org/abs/2007.11824 --------------------------------------
+class MemoryEfficientMish(nn.Module):
+    class F(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_tensors[0]
+            sx = torch.sigmoid(x)
+            fx = F.softplus(x).tanh()
+            return grad_output * (fx + x * sx * (1 - fx * fx))
+
+    def forward(self, x):
+        return self.F.apply(x)
+
+
+# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
 class FReLU(nn.Module):
     def __init__(self, c1, k=3):  # ch_in, kernel
-        super(FReLU, self).__init__()
+        super().__init__()
         self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1)
         self.bn = nn.BatchNorm2d(c1)