initial commit

utils/activations.py

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+import torch
+import torch.functional as F
+import torch.nn as nn
+
+
+# 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):
+    @staticmethod
+    def forward(x):
+        return SwishImplementation.apply(x)
+
+
+class HardSwish(nn.Module):  # https://arxiv.org/pdf/1905.02244.pdf
+    @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)
+
+
+# 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
+    @staticmethod
+    def forward(x):
+        return x * F.softplus(x).tanh()