complete LogAvgExp2D

README.md

@@ -50,7 +50,7 @@ from logavgexp_pytorch import logavgexp
 learned_temp = nn.Parameter(torch.ones(1) * -5).exp().clamp(min = 1e-8) # make sure temperature can't hit 0
 
 x = torch.randn(1, 2048, 5)
-y = logavgexp(x, temp = learned_temp, dim = -1) # (1, 5)
+y = logavgexp(x, temp = learned_temp, dim = 1) # (1, 5)
 ```
 
 Or you can use the `LogAvgExp` class to handle the learned temperature parameter
@@ -69,6 +69,18 @@ x = torch.randn(1, 2048, 5)
 y = logavgexp(x) # (1, 5)
 ```
 
+## LogAvgExp2D
+
+```python
+import torch
+from logavgexp_pytorch import LogAvgExp2D
+
+logavgexp_pool = LogAvgExp2D((2, 2), stride = 2) # (2 x 2) pooling
+
+img = torch.randn(1, 16, 64, 64)
+out = logavgexp_pool(img) # (1, 16, 32, 32)
+```
+
 ## Citations
 
 ```bibtex

logavgexp_pytorch/__init__.py

@@ -1 +1 @@
-from logavgexp_pytorch.logavgexp_pytorch import logavgexp, LogAvgExp
+from logavgexp_pytorch.logavgexp_pytorch import logavgexp, LogAvgExp, LogAvgExp2D

logavgexp_pytorch/logavgexp_pytorch.py

@@ -3,12 +3,24 @@
 from torch import nn
 import torch.nn.functional as F
 
+from einops import rearrange
+
+# helper functions
+
 def exists(t):
     return t is not None
 
 def log(t, eps = 1e-20):
     return torch.log(t + eps)
 
+def cast_tuple(t, length = 1):
+    return t if isinstance(t, tuple) else ((t,) * length)
+
+def calc_conv_output(shape, kernel_size, padding, stride):
+    return tuple(map(lambda x: int((x[0] - x[1] + 2 * x[2]) / x[3] + 1), zip(shape, kernel_size, padding, stride)))
+
+# main function
+
 def logavgexp(
     t,
     mask = None,
@@ -36,6 +48,8 @@ def logavgexp(
     out = out.unsqueeze(dim) if keepdim else out
     return out
 
+# learned temperature - logavgexp class
+
 class LogAvgExp(nn.Module):
     def __init__(
         self,
@@ -71,3 +85,41 @@ def forward(self, x, mask = None, eps = 1e-8):
             temp = temp,
             keepdim = self.keepdim
         )
+
+# logavgexp 2d
+
+class LogAvgExp2D(nn.Module):
+    def __init__(
+        self,
+        kernel_size,
+        *,
+        padding = 0,
+        stride = 1,
+        temp = 0.01,
+        learned_temp = True,
+        eps = 1e-20,
+        **kwargs
+    ):
+        super().__init__()
+        self.padding = cast_tuple(padding, 2)
+        self.stride = cast_tuple(stride, 2)
+        self.kernel_size = cast_tuple(kernel_size, 2)
+
+        self.unfold = nn.Unfold(self.kernel_size, padding = self.padding, stride = self.stride)
+        self.logavgexp = LogAvgExp(dim = -1, eps = eps, learned_temp = learned_temp, temp = temp)
+
+    def forward(self, x):
+        """
+        b - batch
+        c - channels
+        h - height
+        w - width
+        j - reducing dimension
+        """
+
+        b, c, h, w = x.shape
+        out_h, out_w = calc_conv_output((h, w), self.kernel_size, self.padding, self.stride)
+
+        x = self.unfold(x)
+        x = rearrange(x, 'b (c j) (h w) -> b c h w j', h = out_h, w = out_w, c = c)
+        return self.logavgexp(x)

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'logavgexp-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.0.2',
+  version = '0.0.3',
   license='MIT',
   description = 'LogAvgExp - Pytorch',
   author = 'Phil Wang',
@@ -16,6 +16,7 @@
     'logsumexp'
   ],
   install_requires=[
+    'einops>=0.4.1',
     'torch>=1.6'
   ],
   classifiers=[