ml-edu · April 27, 2023 04:51 · Feb 6, 2018
diff --git a/README.md b/README.md
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+
+# Indexed convolutions
+
+A convolution operator over a 1D tensor (BxCxL), where a list of neighbors for each element is provided through a indices tensor (LxK), where K is the size of the convolution kernel. Each row of indices specifies the indices of the K neighbors of the corresponding element in the input. A -1 is handled like for zero padding.
+
+Note that the neighbors specified in indices are not relative, but rather absolute. They have to be specified for each of the elements of the output.
+
+A use case is for convolutions over non-square lattices, such as images on hexagonal lattices coming from Cherenkov telescopes (http://www.isdc.unige.ch/%7Elyard/FirstLight/FirstLight_slowHD.mov).
+
+Example:
+
+```
+import torch
+
+# a 1D input of 5 elems
+input = torch.randn(1,1,5)
+
+# this specifies the indices of neighbors for
+# each elem of the input (a 3 elem kernel here)
+# A -1 corresponds to zero-padding 
+indices = torch.ones(5,3).type(torch.LongTensor)
+
+weight = torch.randn(1,1,3)
+bias = torch.randn(1)
+
+output = torch.nn.functional.indexed_conv(input, indices, weight, bias)
+```
diff --git a/indexed_conv.py b/indexed_conv.py
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+import torch
+from torch.autograd import Variable
+
+def prepare_mask(indices):
+
+    padded = indices == -1
+    indices[padded] = 0
+
+    mask = torch.FloatTensor([1,0])
+    mask = mask[..., padded.t().long()]
+
+    return indices, mask
+
+
+def indexed_conv(input, weight, bias, indices, mask):
+
+    nbatch = input.shape[0]
+    output_width = indices.shape[0]
+    out_chans, in_chans, ksize = weight.shape
+
+    if isinstance(input, Variable):
+        mask = Variable(mask)
+
+    col = input[..., indices.t()] * mask
+    col = col.view(nbatch, -1, output_width)
+
+    weight_col = weight.view(out_chans, -1)
+
+    out = torch.matmul(weight_col, col) + bias
+
+    #print(col)
+    #print(weight_col)
+
+    return out
+
+
+if __name__ == '__main__':
+
+    # input = torch.randn(1,2,5)
+    # weight = torch.randn(1,2,3)
+    # bias = torch.randn(1)
+    # indices = (5 * torch.rand(4,3)).long()
+
+    input = torch.ones(1,2,5)
+    weight = torch.ones(1,2,3)
+    bias = torch.zeros(1)
+    indices = (5 * torch.rand(4,3)).long()
+    indices[0,0] = -1
+
+    indices, mask = prepare_mask(indices)
+
+    print(input)
+    print(indices)
+
+    out = indexed_conv(input, weight, bias, indices, mask)
+
+    input = Variable(input, requires_grad=True)
+    weight = Variable(weight)
+    bias = Variable(bias)
+
+    out = indexed_conv(input, weight, bias, indices, mask)
+
+    print(out)
+
+    out.sum().backward()
+    print(input.grad)
+
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