diff --git a/timm/models/activations.py b/timm/models/activations.py
new file mode 100644
index 00000000..aa29b84d
--- /dev/null
+++ b/timm/models/activations.py
@@ -0,0 +1,180 @@
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+
+_USE_MEM_EFFICIENT_ISH = True
+if _USE_MEM_EFFICIENT_ISH:
+    # This version reduces memory overhead of Swish during training by
+    # recomputing torch.sigmoid(x) in backward instead of saving it.
+    class SwishAutoFn(torch.autograd.Function):
+        """Swish - Described in: https://arxiv.org/abs/1710.05941
+        Memory efficient variant from:
+         https://medium.com/the-artificial-impostor/more-memory-efficient-swish-activation-function-e07c22c12a76
+        """
+        @staticmethod
+        def forward(ctx, x):
+            result = x.mul(torch.sigmoid(x))
+            ctx.save_for_backward(x)
+            return result
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_variables[0]
+            sigmoid_x = torch.sigmoid(x)
+            return grad_output.mul(sigmoid_x * (1 + x * (1 - sigmoid_x)))
+
+    def swish(x, inplace=False):
+        # inplace ignored
+        return SwishAutoFn.apply(x)
+
+
+    class MishAutoFn(torch.autograd.Function):
+        """Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
+        Experimental memory-efficient variant
+        """
+
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            y = x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
+            return y
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_variables[0]
+            x_sigmoid = torch.sigmoid(x)
+            x_tanh_sp = F.softplus(x).tanh()
+            return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))
+
+    def mish(x, inplace=False):
+        # inplace ignored
+        return MishAutoFn.apply(x)
+
+
+    class WishAutoFn(torch.autograd.Function):
+        """Wish: My own mistaken creation while fiddling with Mish. Did well in some experiments.
+        Experimental memory-efficient variant
+        """
+
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            y = x.mul(torch.tanh(torch.exp(x)))
+            return y
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            x = ctx.saved_variables[0]
+            x_exp = x.exp()
+            x_tanh_exp = x_exp.tanh()
+            return grad_output.mul(x_tanh_exp + x * x_exp * (1 - x_tanh_exp * x_tanh_exp))
+
+    def wish(x, inplace=False):
+        # inplace ignored
+        return WishAutoFn.apply(x)
+else:
+    def swish(x, inplace=False):
+        """Swish - Described in: https://arxiv.org/abs/1710.05941
+        """
+        return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid())
+
+
+    def mish(x, inplace=False):
+        """Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
+        """
+        inner = F.softplus(x).tanh()
+        return x.mul_(inner) if inplace else x.mul(inner)
+
+
+    def wish(x, inplace=False):
+        """Wish: My own mistaken creation while fiddling with Mish. Did well in some experiments.
+        """
+        inner = x.exp().tanh()
+        return x.mul_(inner) if inplace else x.mul(inner)
+
+
+class Swish(nn.Module):
+    def __init__(self, inplace=False):
+        super(Swish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return swish(x, self.inplace)
+
+
+class Mish(nn.Module):
+    def __init__(self, inplace=False):
+        super(Mish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return mish(x, self.inplace)
+
+
+class Wish(nn.Module):
+    def __init__(self, inplace=False):
+        super(Wish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return wish(x, self.inplace)
+
+
+def sigmoid(x, inplace=False):
+    return x.sigmoid_() if inplace else x.sigmoid()
+
+
+# PyTorch has this, but not with a consistent inplace argmument interface
+class Sigmoid(nn.Module):
+    def __init__(self, inplace=False):
+        super(Sigmoid, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return x.sigmoid_() if self.inplace else x.sigmoid()
+
+
+def tanh(x, inplace=False):
+    return x.tanh_() if inplace else x.tanh()
+
+
+# PyTorch has this, but not with a consistent inplace argmument interface
+class Tanh(nn.Module):
+    def __init__(self, inplace=False):
+        super(Tanh, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return x.tanh_() if self.inplace else x.tanh()
+
+
+def hard_swish(x, inplace=False):
+    inner = F.relu6(x + 3.).div_(6.)
+    return x.mul_(inner) if inplace else x.mul(inner)
+
+
+class HardSwish(nn.Module):
+    def __init__(self, inplace=False):
+        super(HardSwish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return hard_swish(x, self.inplace)
+
+
+def hard_sigmoid(x, inplace=False):
+    if inplace:
+        return x.add_(3.).clamp_(0., 6.).div_(6.)
+    else:
+        return F.relu6(x + 3.) / 6.
+
+
+class HardSigmoid(nn.Module):
+    def __init__(self, inplace=False):
+        super(HardSigmoid, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return hard_sigmoid(x, self.inplace)
+