#!/usr/bin/env python
-import torch
-from torch import nn
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
######################################################################
-def conv_chain(input_size, output_size, depth, cond):
- if depth == 0:
+
+def conv_chain(input_size, output_size, remain_depth, cond):
+ if remain_depth == 0:
if input_size == output_size:
- return [ [ ] ]
+ return [[]]
else:
- return [ ]
+ return []
else:
- r = [ ]
+ r = []
for kernel_size in range(1, input_size + 1):
- for stride in range(1, input_size + 1):
- if cond(depth, kernel_size, stride):
+ for stride in range(1, input_size):
+ if cond(remain_depth, kernel_size, stride):
n = (input_size - kernel_size) // stride + 1
- if (n - 1) * stride + kernel_size == input_size:
- q = conv_chain(n, output_size, depth - 1, cond)
- r += [ [ (kernel_size, stride) ] + u for u in q ]
+ if (
+ n >= output_size
+ and (n - 1) * stride + kernel_size == input_size
+ ):
+ q = conv_chain(n, output_size, remain_depth - 1, cond)
+ r += [[(kernel_size, stride)] + u for u in q]
return r
+
######################################################################
if __name__ == "__main__":
+ import torch
+ from torch import nn
# Example
c = conv_chain(
- input_size = 64, output_size = 8,
- depth = 5,
+ input_size=64,
+ output_size=8,
+ remain_depth=5,
# We want kernels smaller than 4, strides smaller than the
- # kernels, and stride of 1 except in the two last layers
- cond = lambda d, k, s: k <= 4 and s <= k and (s == 1 or d <= 2)
+ # kernels, and strides of 1 except in the two last layers
+ cond=lambda d, k, s: k <= 4 and s <= k and (s == 1 or d <= 2),
)
x = torch.rand(1, 1, 64)
for m in c:
- model = nn.Sequential(*[ nn.Conv1d(1, 1, l[0], l[1]) for l in m ])
+ model = nn.Sequential(*[nn.Conv1d(1, 1, l[0], l[1]) for l in m])
print(model)
print(x.size(), model(x).size())