#!/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 [ [ ] ]
else:
else:
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)
+ 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,
- cond = lambda d, k, s: k <= 4 and s <= k and (s == 1 or d < 3)
+ remain_depth = 5,
+ # We want kernels smaller than 4, strides smaller than the
+ # 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)