#!/usr/bin/env python
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
import math
from torch import nn
from torch import Tensor
######################################################################
+
def minimal_input_size(w, layer_specs):
- assert w > 0, 'The input is too small'
+ assert w > 0, "The input is too small"
if layer_specs == []:
return w
else:
- k, s = layer_specs[0]
- w = math.ceil((w - k) / s) + 1
- w = minimal_input_size(w, layer_specs[1:])
- return int((w - 1) * s + k)
+ kernel_size, stride = layer_specs[0]
+ v = int(math.ceil((w - kernel_size) / stride)) + 1
+ v = minimal_input_size(v, layer_specs[1:])
+ return (v - 1) * stride + kernel_size
+
######################################################################
# Dummy test
if __name__ == "__main__":
-
- layer_specs = [ (11, 5), (5, 2), (3, 2), (3, 2) ]
+ layer_specs = [(17, 5), (5, 4), (3, 2), (3, 2)]
layers = []
+
for kernel_size, stride in layer_specs:
layers.append(nn.Conv2d(1, 1, kernel_size, stride))